Assessing the Recordkeeping Quality at the School of Dental Sciences, Universiti Sains Malaysia.

INTRODUCTION:  Dental records are an essential part of dental practice. The quality of dental recordkeeping is paramount in ensuring the delivery of high-quality dental care and is also important for medico-legal reasons. Should there be any dispute or need for review, detailed and well-maintained records can provide evidence of the care provided and the decision-making process. OBJECTIVE: The study aimed to assess the quality of dental recordkeeping and dental charting practice at the dental clinic School of Dental Sciences. METHODS: The study was conducted in a retrospective manner reviewing dental records of patients treated by specialists, dental officers, and postgraduate and undergraduate students at the Hospital Universiti Sains Malaysia over a five-year period. Eight key components of clinical dental records i.e. date of charting, legibility on the odontogram, no blank on the odontogram, whether any mistakes have been strikethrough and initials, medical history, dental history, investigation, and treatment plan were assessed. A modified CRABEL scoring system was used to assess the quality of data retrieved from dental records. RESULTS: The study involved the analysis of 324 case files. Among these, 90 files obtained scores ranging from 60% to 80%, with 7.7% attributed to undergraduates, 9.6% to dental officers, 6.8% to postgraduates, and 3.7% to specialists. The remaining 234 files achieved scores between 80% and 100%, with a breakdown of 17% from undergraduates, 15.4% from dental officers, 18.2% from postgraduates, and 21.3% from specialists. CONCLUSION:  Even though the overall quality of recordkeeping in this study is good, with most records achieving a CRABEL score of 80% and above, it's important to acknowledge that ideally, each component assessed should achieve a perfect score of 100%, as it will reflect the practitioners's work.

Open notes in psychotherapy: An exploratory mixed methods survey of psychotherapy students in Switzerland.

Background: In a growing number of countries, patients are offered access to their full online clinical records, including the narrative reports written by clinicians (the latter, referred to as "open notes"). Even in countries with mature patient online record access, access to psychotherapy notes is not mandatory. To date, no research has explored the views of psychotherapy trainees about open notes. Objective: This study aimed to explore the opinions of psychotherapy trainees in Switzerland about patients' access to psychotherapists' free-text summaries. Methods: We administered a web-based mixed methods survey to 201 psychotherapy trainees to explore their familiarity with and opinions about the impact on patients and psychotherapy practice of offering patients online access to their psychotherapy notes. Descriptive statistics were used to analyze the 42-item survey, and qualitative descriptive analysis was employed to examine written responses to four open-ended questions. Results: Seventy-two (35.8%) trainees completed the survey. Quantitative results revealed mixed views about open notes. 75% agreed that, in general open notes were a good idea, and 94.1% agreed that education about open notes should be part of psychotherapy training. When considering impact on patients and psychotherapy, four themes emerged: (a) negative impact on therapy; (b) positive impact on therapy; (c) impact on patients; and (d) documentation. Students identified concerns related to increase in workload, harm to the psychotherapeutic relationship, and compromised quality of records. They also identified many potential benefits including better patient communication and informed consent processes. In describing impact on different therapy types, students believed that open notes might have differential impact depending on the psychotherapy approaches. Conclusions: Sharing psychotherapy notes is not routine but is likely to expand. This mixed methods study provides timely insights into the views of psychotherapy trainees regarding the impact of open notes on patient care and psychotherapy practice.

Structured Textbook Review and Individualized Learning Plans Successfully Remediate Underperforming Residents and Improve General Surgery Program Performance on the ABSITE.

Background: We endeavored to create an evidence-based curriculum to improve general surgery resident fund of knowledge. Global and resident-specific interventions were employed to this end. These interventions were monitored via multiple choice question results on a weekly basis and American Board of Surgery In-Training Examination (ABSITE) performance. Methods: This study was performed in a prospective manner over a 2-year period. A structured textbook review with testing was implemented for all residents. A focused textbook question-writing assignment and a Surgical Council on Resident Education (SCORE)-based individualized learning plan (ILP) were implemented for residents scoring below the 35th percentile on the ABSITE. Results: Curriculum implementation resulted in a statistically significant reduction in the number of residents scoring below the 35th percentile, from 50% to 30.8% (P = .023). One hundred percent of residents initially scoring below the 35th percentile were successfully remediated over the study period. Average overall program ABSITE percentile scores increased from 38.5% to 51.4% over a 2-year period. Conclusion: Structured textbook review and testing combined with a question-writing assignment and a SCORE-focused ILP successfully remediated residents scoring below the 35th percentile and improved general surgery residency ABSITE performance.

Comparing the Effect of Early Versus Delayed Metformin Treatment on Glycaemic Parameters Among Australian Adults With Incident Diabetes: Evidence Using a National General Practice Database.

PURPOSE: To compare the effect of early vs delayed metformin treatment for glycaemic management among patients with incident diabetes. METHODS: Cohort study using electronic health records of regular patients (1+ visits per year in 3 consecutive years) aged 40+ years with 'incident' diabetes attending Australian general practices (MedicineInsight, 2011-2018). Patients with incident diabetes were defined as those who had a) 12+ months of medical data before the first recording of a diabetes diagnosis AND b) a diagnosis of 'diabetes' recorded at least twice in their electronic medical records or a diagnosis of 'diabetes' recorded only once combined with at least 1 abnormal glycaemic result (i.e., HbA1c ≥6.5%, fasting blood glucose [FBG] ≥7.0 mmol/L, or oral glucose tolerance test ≥11.1mmol/L) in the preceding 3 months. The effect of early (<3 months), timely (3-6 months), or delayed (6-12 months) initiation of metformin treatment vs no metformin treatment within 12 months of diagnosis on HbA1c and FBG levels 3 to 24 months after diagnosis was compared using linear regression and augmented inverse probability weighted models. Patients initially managed with other antidiabetic medications (alone or combined with metformin) were excluded. FINDINGS: Of 18,856 patients with incident diabetes, 38.8% were prescribed metformin within 3 months, 3.9% between 3 and 6 months, and 6.2% between 6 and 12 months after diagnosis. The untreated group had the lowest baseline parameters (mean HbA1c 6.4%; FBG 6.9mmol/L) and maintained steady levels throughout follow-up. Baseline glycaemic parameters for those on early treatment with metformin (<3 months since diagnosis) were the highest among all groups (mean HbA1c 7.6%; FBG 8.8mmol/L), reaching controlled levels at 3 to 6 months (mean HbA1c 6.5%; FBG 6.9mmol/L) with sustained improvement until the end of follow-up (mean HbA1c 6.4%; FBG 6.9mmol/L at 18-24 months). Patients with timely and delayed treatment also improved their glycaemic parameters after initiating treatment (timely treatment: mean HbA1c 7.3% and FBG 8.3mmol/L at 3-6 months; 6.6% and 6.9mmol/L at 6-12 months; delayed treatment: mean HbA1c 7.2% and FBG 8.4mmol/L at 6-12 months; 6.7% and 7.1mmol/L at 12-18 months). Compared to those not managed with metformin, the corresponding average treatment effect for HbA1c at 18-24 months was +0.04% (95%CI -0.05;0.10) for early, +0.24% (95%CI 0.11;0.37) for timely, and +0.29% (95%CI 0.20;0.39) for delayed treatment. IMPLICATIONS: Early metformin therapy (<3 months) for patients recently diagnosed with diabetes consistently improved HbA1c and FBG levels in the first 24 months of diagnosis.

A real-world exploration into clinical outcomes of direct oral anticoagulant therapy in people with chronic kidney disease: a large hospital-based study.

BACKGROUND: There is limited evidence to support definite clinical outcomes of direct oral anticoagulant (DOAC) therapy in chronic kidney disease (CKD). By identifying the important variables associated with clinical outcomes following DOAC administration in patients in different stages of CKD, this study aims to assess this evidence gap. METHODS: An anonymised dataset comprising 97,413 patients receiving DOAC therapy in a tertiary health setting was systematically extracted from the multidimensional electronic health records and prepared for analysis. Machine learning classifiers were applied to the prepared dataset to select the important features which informed covariate selection in multivariate logistic regression analysis. RESULTS: For both CKD and non-CKD DOAC users, features such as length of stay, treatment days, and age were ranked highest for relevance to adverse outcomes like death and stroke. Patients with Stage 3a CKD had significantly higher odds of ischaemic stroke (OR 2.45, 95% Cl: 2.10-2.86; p = 0.001) and lower odds of all-cause mortality (OR 0.87, 95% Cl: 0.79-0.95; p = 0.001) on apixaban therapy. In patients with CKD (Stage 5) receiving apixaban, the odds of death were significantly lowered (OR 0.28, 95% Cl: 0.14-0.58; p = 0.001), while the effect on ischaemic stroke was insignificant. CONCLUSIONS: A positive effect of DOAC therapy was observed in advanced CKD. Key factors influencing clinical outcomes following DOAC administration in patients in different stages of CKD were identified. These are crucial for designing more advanced studies to explore safer and more effective DOAC therapy for the population.

Forecasting Acute Kidney Injury and Resource Utilization in ICU patients using longitudinal, multimodal models.

Background: Advances in artificial intelligence (AI) have realized the potential of revolutionizing healthcare, such as predicting disease progression via longitudinal inspection of Electronic Health Records (EHRs) and lab tests from patients admitted to Intensive Care Units (ICU). Although substantial literature exists addressing broad subjects, including the prediction of mortality, length-of-stay, and readmission, studies focusing on forecasting Acute Kidney Injury (AKI), specifically dialysis anticipation like Continuous Renal Replacement Therapy (CRRT) are scarce. The technicality of how to implement AI remains elusive. Objective: This study aims to elucidate the important factors and methods that are required to develop effective predictive models of AKI and CRRT for patients admitted to ICU, using EHRs in the Medical Information Mart for Intensive Care (MIMIC) database. Methods: We conducted a comprehensive comparative analysis of established predictive models, considering both time-series measurements and clinical notes from MIMIC-IV databases. Subsequently, we proposed a novel multi-modal model which integrates embeddings of top-performing unimodal models, including Long Short-Term Memory (LSTM) and BioMedBERT, and leverages both unstructured clinical notes and structured time series measurements derived from EHRs to enable the early prediction of AKI and CRRT. Results: Our multimodal model achieved a lead time of at least 12 hours ahead of clinical manifestation, with an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.888 for AKI and 0.997 for CRRT, as well as an Area Under the Precision Recall Curve (AUPRC) of 0.727 for AKI and 0.840 for CRRT, respectively, which significantly outperformed the baseline models. Additionally, we performed a SHapley Additive exPlanation (SHAP) analysis using the expected gradients algorithm, which highlighted important, previously underappreciated predictive features for AKI and CRRT. Conclusion: Our study revealed the importance and the technicality of applying longitudinal, multimodal modeling to improve early prediction of AKI and CRRT, offering insights for timely interventions. The performance and interpretability of our model indicate its potential for further assessment towards clinical applications, to ultimately optimize AKI management and enhance patient outcomes.

Assessment of the Capability to Accurately Capture the Vertical Relationship by the Various Bite Registration Materials.

Background: This research study aimed to evaluate and compare the capability of four various bite registration materials to reproduce precise interocclusal relationships in the vertical dimension. Materials and Methods: Ideal maxillary and mandibular casts were mounted on the semi-adjustable articulator in maximum intercuspation after mock tooth preparation on 46, 47, and 48. Models were scanned by the Medit T500 Dental Lab Scanner, and initial reading was noted at the predetermined points. Ten interocclusal bite registrations were made using four materials (CADbite, Jet Bite, Ramitec, and Aluwax). The mandibular model was demounted and again remounted using the interocclusal records, and the final reading was noted after scanning. Results: Ramitec showed superior results when compared to polyvinyl bite registration material and Aluwax, but the differences between Ramitec, CADbite, and Jet Bite were nonsignificant. Conclusions: Although all four materials are suitable for clinical use, elastomeric materials showed superior results. In that, polyether was found to be the best.

Timing of antibiotic treatment identifies distinct clinical presentations among patients presenting with suspected septic shock.

Objective: Recent clinical guidelines for sepsis management emphasize immediate antibiotic initiation for suspected septic shock. Though hypotension is a high-risk marker of sepsis severity, prior studies have not considered the precise timing of hypotension in relation to antibiotic initiation and how clinical characteristics and outcomes may differ. Our objective was to evaluate antibiotic initiation in relation to hypotension to characterize differences in sepsis presentation and outcomes in patients with suspected septic shock. Methods: Adults presenting to the emergency department (ED) June 2012-December 2018 diagnosed with sepsis (Sepsis-III electronic health record [EHR] criteria) and hypotension (non-resolving for ≥30 min, systolic blood pressure <90 mmHg) within 24 h. We categorized patients who received antibiotics before hypotension ("early"), 0-60 min after ("immediate"), and >60 min after ("late") treatment. Results: Among 2219 patients, 55% received early treatment, 13% immediate, and 32% late. The late subgroup often presented to the ED with hypotension (median 0 min) but received antibiotics a median of 191 min post-ED presentation. Clinical characteristics notable for this subgroup included higher prevalence of heart failure and liver disease (p < 0.05) and later onset of systemic inflammatory response syndrome (SIRS) criteria compared to early/immediate treatment subgroups (median 87 vs. 35 vs. 20 min, p < 0.0001). After adjustment, there was no difference in clinical outcomes among treatment subgroups. Conclusions: There was significant heterogeneity in presentation and timing of antibiotic initiation for suspected septic shock. Patients with later treatment commonly had hypotension on presentation, had more hypotension-associated comorbidities, and developed overt markers of infection (eg, SIRS) later. While these factors likely contribute to delays in clinician recognition of suspected septic shock, it may not impact sepsis outcomes.

'Kindling the fire' of NHS patient data exploitations: The care.data controversy in news media discourses.

This paper explores news media discourse about care.data: an NHS England programme of work for amalgamating and sharing patient data from primary care for planning and research. It was scrapped in 2016 after three years of public outcry, delays and around 1.5 million opt-outs. I examine UK news media coverage of this programme through the 'fire object' metaphor, focusing upon the visions of purpose and value it inspired, the abrupt discontinuities, juxtapositions and transformations it performed, and the matters of concern that went unheeded. Findings suggest that, in care.data's pursuit of a societal consensus on NHS patient data exploitations, various visions for new and fluid data flows brought to presence narratives of transforming the NHS, saving lives, and growing the economy. Other realities and concerns that mattered for certain stakeholders, such as data ownership and commercialisation, public engagement and informed consent, commitment and leadership, operational capabilities, and NHS privatisation agendas, remained absent or unsettled. False dichotomies kept the controversy alive, sealing its fate. I conclude by arguing that such failed programmes can turn into phantom-like objects, haunting future patient data schemes of similar aspirations. The paper highlights the role news media can have in understanding such energetic public controversies.

Central nervous system medication use around hospitalization.

BACKGROUND: Central nervous system (CNS) medication use is common among older adults, yet the impact of hospitalizations on use remains unclear. This study details CNS medication use, discontinuations, and user profiles during hospitalization periods. METHODS: Retrospective cohort study using electronic health records on patients ≥65 years, from three hospitals (2018-2020), and prescribed a CNS medication around hospitalization (90 days prior to 90 days after). Latent class transitions analysis (LCTA) examined profiles of CNS medication class users across four time points (90 days prior, admission, discharge, 90 days after hospitalization). RESULTS: Among 4666 patients (mean age 74.3 ± 9.3 years; 63% female; 70% White; mean length of stay 4.6 ± 5.6 days (median 3.0 [2.0, 6.0]), the most commonly prescribed CNS medications were antidepressants (56%) and opioids (49%). Overall, 74% (n = 3446) of patients were persistent users of a CNS medication across all four time points; 7% (n = 388) had discontinuations during hospitalization, but of these, 64% (216/388) had new starts or restarts within 90 days after hospitalization. LCTA identified three profile groups: (1) low CNS medication users, 54%-60% of patients; (2) mental health medication users, 30%-36%; and (3) acute/chronic pain medication users, 9%-10%. Probability of staying in same group across the four time points was high (0.88-1.00). Transitioning to the low CNS medication use group was highest from admission to discharge (probability of 9% for pain medication users, 5% for mental health medication users). Female gender increased (OR 2.4, 95% CI 1.3-4.3), while chronic kidney disease lowered (OR 0.5, 0.2-0.9) the odds of transitioning to the low CNS medication use profile between admission and discharge. CONCLUSIONS: CNS medication use stays consistent around hospitalization, with discontinuation more likely between admission and discharge, especially among pain medication users. Further research on patient outcomes is needed to understand the benefits and harms of hospital deprescribing, particularly for medications requiring gradual tapering.

The biomarkers ATLAS: An audit on 1100 non-small cell lung cancer from an Italian knowledge-based database.

AIMS: To date, precision medicine has revolutionized the clinical management of Non-Small Cell Lung Cancer (NSCLC). International societies approved a rapidly improved mandatory testing biomarkers panel for the clinical stratification of NSCLC patients, but harmonized procedures are required to optimize the diagnostic workflow. In this context a knowledge-based database (Biomarkers ATLAS, https://biomarkersatlas.com/) was developed by a supervising group of expert pathologists and thoracic oncologists collecting updated clinical and molecular records from about 80 referral Italian institutions. Here, we audit molecular and clinical data from n = 1100 NSCLC patients collected from January 2019 to December 2020. METHODS: Clinical and molecular records from NSCLC patients were retrospectively collected from the two coordinating institutions (University of Turin and University of Naples). Molecular biomarkers (KRAS, EGFR, BRAF, ROS1, ALK, RET, NTRK, MET) and clinical data (sex, age, histological type, smoker status, PD-L1 expression, therapy) were collected and harmonized. RESULTS: Clinical and molecular data from 1100 (n = 552 mutated and n = 548 wild-type) NSCLC patients were systematized and annotated in the ATLAS knowledge-database. Molecular records from biomarkers testing were matched with main patients' clinical variables. CONCLUSIONS: Biomarkers ATLAS (https://biomarkersatlas.com/) represents a unique, easily managing, and reliable diagnostic tool aiming to integrate clinical records with molecular alterations of NSCLC patients in the real-word Italian scenario.

Using interoperable nursing care data to improve outcomes for multiple traumas patients with Impaired Physical Mobility.

AIM(S): To demonstrate how interoperable nursing care data can be used by nurses to create a more holistic understanding of the healthcare needs of multiple traumas patients with Impaired Physical Mobility. By proposing and validating linkages for the nursing diagnosis of Impaired Physical Mobility in multiple trauma patients by mapping to the Nursing Outcomes Classification (NOC) and Nursing Interventions Classification (NIC) equivalent terms using free-text nursing documentation. DESIGN: A descriptive cross-sectional design, combining quantitative analysis of interoperable data sets and the Kappa's coefficient score with qualitative insights from cross-mapping methodology and nursing professionals' consensus. METHODS: Cross-mapping methodology was conducted in a Brazilian Level 1 Trauma Center using de-identified records of adult patients with a confirmed medical diagnosis of multiple traumas and Impaired Physical Mobility (a nursing diagnosis). The hospital nursing free-text records were mapped to NANDA-I, NIC, NOC and NNN linkages were identified. The data records were retrieved for admissions from September to October 2020 and involved medical and nursing records. Three expert nurses evaluated the cross-mapping and linkage results using a 4-point Likert-type scale and Kappa's coefficient. RESULTS: The de-identified records of 44 patients were evaluated and then were mapped to three NOCs related to nurses care planning: (0001) Endurance; (0204) Immobility Consequences: Physiological, and (0208) Mobility and 13 interventions and 32 interrelated activities: (6486) Environmental Management: Safety; (0840) Positioning; (3200) Aspiration Precautions; (1400) Pain Management; (0940) Traction/Immobilization Care; (3540) Pressure Ulcer Prevention; (3584) Skincare: Topical Treatment; (1100) Nutrition Management; (3660) Wound Care; (1804) Self-Care Assistance: Toileting; (1801) Self-Care Assistance: Bathing/Hygiene; (4130) Fluid Monitoring; and (4200) Intravenous Therapy. The final version of the constructed NNN Linkages identified 37 NOCs and 41 NICs. CONCLUSION: These valid NNN linkages for patients with multiple traumas can serve as a valuable resource that enables nurses, who face multiple time constraints, to make informed decisions efficiently. This approach of using evidence-based linkages like the one developed in this research holds high potential for improving patient's safety and outcomes. NO PATIENT OR PUBLIC CONTRIBUTION: In this study, there was no direct involvement of patients, service users, caregivers or public members in the design, conduct, analysis and interpretation of data or preparation of the manuscript. The study focused solely on analysing existing de-identified medical and nursing records to propose and validate linkages for nursing diagnoses.

OpenSAFELY: The impact of COVID-19 on azathioprine, leflunomide and methotrexate monitoring, and factors associated with change in monitoring rate.

AIMS: The COVID-19 pandemic created unprecedented pressure on healthcare services. This study investigates whether disease-modifying antirheumatic drug (DMARD) safety monitoring was affected during the COVID-19 pandemic. METHODS: A population-based cohort study was conducted using the OpenSAFELY platform to access electronic health record data from 24.2 million patients registered at general practices using TPP's SystmOne software. Patients were included for further analysis if prescribed azathioprine, leflunomide or methotrexate between November 2019 and July 2022. Outcomes were assessed as monthly trends and variation between various sociodemographic and clinical groups for adherence with standard safety monitoring recommendations. RESULTS: An acute increase in the rate of missed monitoring occurred across the study population (+12.4 percentage points) when lockdown measures were implemented in March 2020. This increase was more pronounced for some patient groups (70-79 year-olds: +13.7 percentage points; females: +12.8 percentage points), regions (North West: +17.0 percentage points), medications (leflunomide: +20.7 percentage points) and monitoring tests (blood pressure: +24.5 percentage points). Missed monitoring rates decreased substantially for all groups by July 2022. Consistent differences were observed in overall missed monitoring rates between several groups throughout the study. CONCLUSION: DMARD monitoring rates temporarily deteriorated during the COVID-19 pandemic. Deterioration coincided with the onset of lockdown measures, with monitoring rates recovering rapidly as lockdown measures were eased. Differences observed in monitoring rates between medications, tests, regions and patient groups highlight opportunities to tackle potential inequalities in the provision or uptake of monitoring services. Further research should evaluate the causes of the differences identified between groups.

Variations in Electronic Health Record-Based Definitions of Diabetic Retinopathy Cohorts: A Literature Review and Quantitative Analysis.

Purpose: Use of the electronic health record (EHR) has motivated the need for data standardization. A gap in knowledge exists regarding variations in existing terminologies for defining diabetic retinopathy (DR) cohorts. This study aimed to review the literature and analyze variations regarding codified definitions of DR. Design: Literature review and quantitative analysis. Subjects: Published manuscripts. Methods: Four graders reviewed PubMed and Google Scholar for peer-reviewed studies. Studies were included if they used codified definitions of DR (e.g., billing codes). Data elements such as author names, publication year, purpose, data set type, and DR definitions were manually extracted. Each study was reviewed by ≥ 2 authors to validate inclusion eligibility. Quantitative analyses of the codified definitions were then performed to characterize the variation between DR cohort definitions. Main Outcome Measures: Number of studies included and numeric counts of billing codes used to define codified cohorts. Results: In total, 43 studies met the inclusion criteria. Half of the included studies used datasets based on structured EHR data (i.e., data registries, institutional EHR review), and half used claims data. All but 1 of the studies used billing codes such as the International Classification of Diseases 9th or 10th edition (ICD-9 or ICD-10), either alone or in addition to another terminology for defining disease. Of the 27 included studies that used ICD-9 and the 20 studies that used ICD-10 codes, the most common codes used pertained to the full spectrum of DR severity. Diabetic retinopathy complications (e.g., vitreous hemorrhage) were also used to define some DR cohorts. Conclusions: Substantial variations exist among codified definitions for DR cohorts within retrospective studies. Variable definitions may limit generalizability and reproducibility of retrospective studies. More work is needed to standardize disease cohorts. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Validation of GPT-4 for clinical event classification: A comparative analysis with ICD codes and human reviewers.

BACKGROUND AND AIM: Effective clinical event classification is essential for clinical research and quality improvement. The validation of artificial intelligence (AI) models like Generative Pre-trained Transformer 4 (GPT-4) for this task and comparison with conventional methods remains unexplored. METHODS: We evaluated the performance of the GPT-4 model for classifying gastrointestinal (GI) bleeding episodes from 200 medical discharge summaries and compared the results with human review and an International Classification of Diseases (ICD) code-based system. The analysis included accuracy, sensitivity, and specificity evaluation, using ground truth determined by physician reviewers. RESULTS: GPT-4 exhibited an accuracy of 94.4% in identifying GI bleeding occurrences, outperforming ICD codes (accuracy 63.5%, P < 0.001). GPT-4's accuracy was either slightly lower or statistically similar to individual human reviewers (Reviewer 1: 98.5%, P < 0.001; Reviewer 2: 90.8%, P = 0.170). For location classification, GPT-4 achieved accuracies of 81.7% and 83.5% for confirmed and probable GI bleeding locations, respectively, with figures that were either slightly lower or comparable with those of human reviewers. GPT-4 was highly efficient, analyzing the dataset in 12.7 min at a cost of 21.2 USD, whereas human reviewers required 8-9 h each. CONCLUSION: Our study indicates GPT-4 offers a reliable, cost-efficient, and faster alternative to current clinical event classification methods, outperforming the conventional ICD coding system and performing comparably to individual expert human reviewers. Its implementation could facilitate more accurate and granular clinical research and quality audits. Future research should explore scalability, prompt and model tuning, and ethical implications of high-performance AI models in clinical data processing.

Adhesive restorations in primary dentition: A retrospective analysis of survival rate and associated factors.

BACKGROUND: Several clinical and individual factors may play a role in the survival rate of dental restorations, such as characteristics related to the child's age and oral hygiene, and factors associated with the tooth, such as the type of material and number of surfaces to be restored. AIM: To analyse the survival rate of adhesive restorations on primary teeth and factors associated with restoration survival. DESIGN: The study included dental records of children aged 3-12 years having received adhesive restorations on primary teeth at a Brazilian dental school between 2009 and 2019. A Kaplan-Meier survival curve was used to plot survival rates using the log-rank test. A multivariate Cox regression model was run to identify individual and dental factors associated with restoration failure. RESULTS: The sample comprised 269 restored teeth in 111 children. Survival curves were similar for all materials (p = .20) and types of isolation (p = .05). The annual failure rate was 3.60% for glass ionomer cement, 1.23% for resin-modified glass ionomer cement and 0.40% for composite resin. The following variables were associated with more failures: Class II restoration compared with Class I (HR = 1.96; 95%CI: 1.28-2.99, p < .001), proportion of decayed teeth (HR = 11.89; 95%CI: 2.80-50.57, p < .001) and child's age (HR = 1.17; 95%CI: 1.06-1.29, p < .001). CONCLUSION: The different materials and types of isolation had similar survival rates. Children with more decayed teeth have an increased risk of restoration failure.

Missed opportunities in heart failure diagnosis and management: study of an urban UK population.

AIMS: This study aimed to examine the diagnostic pathways and outcomes of patients with heart failure (HF), stratified by left ventricular ejection fraction (EF), and to highlight deficiencies in real-world HF diagnosis and management. METHODS AND RESULTS: We conducted a retrospective cohort study in Salford, United Kingdom, utilizing linked primary and secondary care data for HF patients diagnosed between January 2010 and November 2019. We evaluated characteristics, diagnostic patterns, healthcare resource utilization, and outcomes. Patients were categorized according to baseline (the latest measure prior to or within 90 days post-diagnosis) as having HF with reduced EF (HFrEF), mildly reduced EF (HFmrEF), or preserved EF (HFpEF). The data encompassed a 2 year period before diagnosis and up to 5 years post-diagnosis. A total of 3227 patients were diagnosed with HF between January 2010 and November 2019. The mean follow-up time was 2.6 [±1.9 standard deviation (SD)] years. The mean age at diagnosis was 74.8 (±12.7 SD) years, and 1469 (45.5%) were female. HFpEF was the largest cohort (46.6%, npEF = 1505), HFmrEF constituted 16.1% (nmrEF = 520), and HFrEF 18.5% (nrEF = 596) of the population, while 18.8% (nu = 606) of patients remained unassigned due to insufficient evidence to support categorization. At baseline, measurement of natriuretic peptide (NP; brain NP and N-terminal pro-B-type NP) and echocardiographic report data were available for 592 (18.3%) and 2621 (81.2%) patients, respectively. A total of 2099 (65.0%) of the HF cohort had access to a cardiology-led outpatient clinic prior to the HF diagnosis, and 602 (18.7%) attended cardiac rehabilitation post-diagnosis. The 5 year crude survival rate was 37.8% [95% confidence interval (CI) (35.2-40.7%)], 42.3% [95% CI (38.0-47.2%)], and 45.5% [95% CI (41.0-50.4%)] for HFpEF, HFrEF, and HFmrEF, respectively. CONCLUSIONS: Low survival rates were observed across all HF groups, along with suboptimal rates of NP testing and specialist assessments. These findings suggest missed opportunities for timely and accurate HF diagnosis, a pivotal first step in improving outcomes for HF patients. Addressing these gaps in diagnosis and management is urgently needed.

Illuminating the landscape of high-level clinical trial opportunities in the All of Us Research Program.

OBJECTIVE: With its size and diversity, the All of Us Research Program has the potential to power and improve representation in clinical trials through ancillary studies like Nutrition for Precision Health. We sought to characterize high-level trial opportunities for the diverse participants and sponsors of future trial investment. MATERIALS AND METHODS: We matched All of Us participants with available trials on ClinicalTrials.gov based on medical conditions, age, sex, and geographic location. Based on the number of matched trials, we (1) developed the Trial Opportunities Compass (TOC) to help sponsors assess trial investment portfolios, (2) characterized the landscape of trial opportunities in a phenome-wide association study (PheWAS), and (3) assessed the relationship between trial opportunities and social determinants of health (SDoH) to identify potential barriers to trial participation. RESULTS: Our study included 181 529 All of Us participants and 18 634 trials. The TOC identified opportunities for portfolio investment and gaps in currently available trials across federal, industrial, and academic sponsors. PheWAS results revealed an emphasis on mental disorder-related trials, with anxiety disorder having the highest adjusted increase in the number of matched trials (59% [95% CI, 57-62]; P < 1e-300). Participants from certain communities underrepresented in biomedical research, including self-reported racial and ethnic minorities, had more matched trials after adjusting for other factors. Living in a nonmetropolitan area was associated with up to 13.1 times fewer matched trials. DISCUSSION AND CONCLUSION: All of Us data are a valuable resource for identifying trial opportunities to inform trial portfolio planning. Characterizing these opportunities with consideration for SDoH can provide guidance on prioritizing the most pressing barriers to trial participation.

Hybrid artificial intelligence outcome prediction using features extraction from stress perfusion cardiac magnetic resonance images and electronic health records.

Background: Prediction of clinical outcomes in coronary artery disease (CAD) has been conventionally achieved using clinical risk factors. The relationship between imaging features and outcome is still not well understood. This study aims to use artificial intelligence to link image features with mortality outcome. Methods: A retrospective study was performed on patients who had stress perfusion cardiac magnetic resonance (SP-CMR) between 2011 and 2021. The endpoint was all-cause mortality. Convolutional neural network (CNN) was used to extract features from stress perfusion images, and multilayer perceptron (MLP) to extract features from electronic health records (EHRs), both networks were concatenated in a hybrid neural network (HNN) to predict study endpoint. Image CNN was trained to predict study endpoint directly from images. HNN and image CNN were compared with a linear clinical model using area under the curve (AUC), F1 scores, and McNemar's test. Results: Total of 1,286 cases were identified, with 201 death events (16%). The clinical model had good performance (AUC =80%, F1 score =37%). Best Image CNN model showed AUC =72% and F1 score =38%. HNN outperformed the other two models (AUC =82%, F1 score =43%). McNemar's test showed statistical difference between image CNN and both clinical model (P<0.01) and HNN (P<0.01). There was no significant difference between HNN and clinical model (P=0.15). Conclusions: Death in patients with suspected or known CAD can be predicted directly from stress perfusion images without clinical knowledge. Prediction can be improved by HNN that combines clinical and SP-CMR images.