Machine learning applications in craniosynostosis diagnosis and treatment prediction: a systematic review.

Craniosynostosis refers to the premature fusion of one or more of the fibrous cranial sutures connecting the bones of the skull. Machine learning (ML) is an emerging technology and its application to craniosynostosis detection and management is underexplored. This systematic review aims to evaluate the application of ML techniques in the diagnosis, severity assessment, and predictive modeling of craniosynostosis. A comprehensive search was conducted on the PubMed and Google Scholar databases using predefined keywords related to craniosynostosis and ML. Inclusion criteria encompassed peer-reviewed studies in English that investigated ML algorithms in craniosynostosis diagnosis, severity assessment, or treatment outcome prediction. Three independent reviewers screened the search results, performed full-text assessments, and extracted data from selected studies using a standardized form. Thirteen studies met the inclusion criteria and were included in the review. Of the thirteen papers examined on the application of ML to the identification and treatment of craniosynostosis, two papers were dedicated to sagittal craniosynostosis, five papers utilized several different types of craniosynostosis in the training and testing of their ML models, and six papers were dedicated to metopic craniosynostosis. ML models demonstrated high accuracy in identifying different types of craniosynostosis and objectively quantifying severity using innovative metrics such as metopic severity score and cranial morphology deviation. The findings highlight the significant strides made in utilizing ML techniques for craniosynostosis diagnosis, severity assessment, and predictive modeling. Predictive modeling of treatment outcomes following surgical interventions showed promising results, aiding in personalized treatment strategies. Despite methodological diversities among studies, the collective evidence underscores ML's transformative potential in revolutionizing craniosynostosis management.

Trends in engagement with cognitive-behavioral therapy for chronic pain conditions after referral.

Cognitive-behavioral therapy for chronic pain (CBT-CP) is an important evidence-based non-pharmacologic treatment for chronic back and neck pain that is frequently recommended as a component of multidisciplinary treatment. However, the success of CBP-CP's implementation in clinical settings is affected by a variety of poorly understood obstacles to patient engagement with CBT-CP. Expanding upon the limited prior research conducted in heterogeneous practice settings, this study examines patterns of treatment initiation for CBT-CP at an interdisciplinary, hospital-based chronic pain practice and conducts exploratory comparisons between groups of patients who did and did not engage in CBT-CP after receiving a referral. Patients' descriptive data, including pain severity, work status, prior therapy, and behavioral health questionnaire scores at intake visit, were obtained through a retrospective chart review of electronic medical records. Data were then analyzed using inter-group comparisons and logistic regression modeling to determine factors that predicted treatment initiation for CBT-CP. On multivariate analysis, we found that patient's depression level as measured by their Patient Health Questionnaire 9 (PHQ-9) score was solely predictive of treatment initiation, as chronic pain patients with a higher level of depression were found to be more likely to attend their recommended appointments of CBT-CP. Anxiety score as measured by GAD-7, work status, pain scores, and prior therapy engagement were not independently predictive. No single "profile" of patient-level factors was found to delineate patients who did and did not initiate CBT-CP, demonstrating the limitations of clinical variables as predictors of uptake.

Cognitive-behavioral therapy (CBT) is a frequently used therapy option, and can be helpful for patients with chronic low back and/or neck pain. However, patients do not always choose to engage in CBT when offered in the context of chronic pain. Reasons patients choose not to pursue CBT, when recommended, are not well understood. This study used data from a hospital-based chronic pain practice in order to identify reasons that patients choose to begin CBT and those who do not. Data about these patients was collected from electronic medical records (EMRs) and was used to conduct statistical analyses, with the goal of determining what factors were significantly different between the two groups of patients. We identified that patients who have more severe depression symptoms based on a specific mental health questionnaire (the Patient Health Questionnaire 9, or PHQ-9) were more likely to engage with CBT. Study results imply that patients without comorbid depression may benefit from additional counseling on the potential benefits of CBT in the management of chronic pain. These results also suggest that reasons other than clinical factors are impacting whether or not patients engage with CBT.

Chat-GPT on brain tumors: An examination of Artificial Intelligence/Machine Learning's ability to provide diagnoses and treatment plans for example neuro-oncology cases.

OBJECTIVE: Assess the capabilities of ChatGPT-3.5 and 4 to provide accurate diagnoses, treatment options, and treatment plans for brain tumors in example neuro-oncology cases. METHODS: ChatGPT-3.5 and 4 were provided with twenty example neuro-oncology cases of brain tumors, all selected from medical textbooks. The artificial intelligence programs were asked to give a diagnosis, treatment option, and treatment plan for each of these twenty example cases. Team members first determined in which cases ChatGPT-3.5 and 4 provided the correct diagnosis or treatment plan. Twenty neurosurgeons from the researchers' institution then independently rated the diagnoses, treatment options, and treatment plans provided by both artificial intelligence programs for each of the twenty example cases, on a scale of one to ten, with ten being the highest score. To determine whether the difference between the scores of ChatGPT-3.5 and 4 was statistically significant, a paired t-test was conducted for the average scores given to the programs for each example case. RESULTS: In the initial analysis of correct responses, ChatGPT-4 had an accuracy of 85% for its diagnoses of example brain tumors and an accuracy of 75% for its provided treatment plans, while ChatGPT-3.5 only had an accuracy of 65% and 10%, respectively. The average scores given by the twenty independent neurosurgeons to ChatGPT-4 for its accuracy of diagnosis, provided treatment options, and provided treatment plan were 8.3, 8.4, and 8.5 out of 10, respectively, while ChatGPT-3.5's average scores for these categories of assessment were 5.9, 5.7, and 5.7. These differences in average score are statistically significant on a paired t-test, with a p-value of less than 0.001 for each difference. CONCLUSIONS: ChatGPT-4 demonstrates great promise as a diagnostic tool for brain tumors in neuro-oncology, as attested to by the program's performance in this study and its assessment by surveyed neurosurgeon reviewers.

Emergency Absentee Voting for Hospitalized Patients and Voting During COVID-19: A 50-State Study.

INTRODUCTION: Voters facing illness or disability are disproportionately under-represented in terms of voter turnout. Earlier research has indicated that enfranchisement of these populations may reinforce the implementation of policies improving health outcomes and equity. Due to the confluence of the coronavirus 2019 (COVID-19) pandemic and the 2020 election, we aimed to assess emergency absentee voting processes, which allow voters hospitalized after regular absentee deadlines to still obtain an absentee ballot, and election changes due to COVID-19 in all 50 states. METHODS: We performed a cross-sectional study collecting 34 variables pertaining to emergency voting processes and COVID-19-related election changes, including deadlines, methods of submission for applications and ballots, and specialized services for patients. Data were obtained from, in order of priority, state boards of elections websites, poll worker manuals, application forms, and state legislation. We verified all data through direct correspondence with state boards of elections. RESULTS: Emergency absentee voting processes are in place in 39 states, with the remaining states having universal vote-by-mail (n = 5) or extended regular absentee voting deadlines (n = 6). The emergency absentee period most commonly began within 24 hours following the normal absentee application deadline, which was often seven days before an election (n = 11). Unique aspects of emergency voting processes included patients designating an "authorized agent" to deliver their applications and ballots (n = 38), electronic ballot delivery (n = 5), and in-person teams that deliver ballots directly to patients (n = 18). Documented barriers in these processes nationwide include unavailable online information (n = 11), restrictions mandating agents to be family members (n = 7), physician affidavits or signatures (n = 9), and notary or witness signature requirements (n = 15). For the November 2020 presidential election, 12 states expanded absentee eligibility to allow COVID-19 as a reason to request an absentee ballot, and 18 states mailed absentee ballot applications or absentee ballots to all registered voters. CONCLUSION: While 39 states operate emergency absentee voting processes for hospitalized voters, there are considerable areas for improvement and heterogeneity in guidelines for these protocols. For future election cycles, information on emergency voting and broader election reforms due to COVID-19 may be useful for emergency providers and patients alike to improve the democratic participation of voters experiencing illness.

Emergency Mail-in Voting in Rhode Island: Protecting Civic Participation During COVID-19 and Beyond.

The COVID-19 pandemic challenges safe and equitable voting in the United States' 2020 elections, and in response, several states including Rhode Island (RI) have made significant changes to election policy. In addition to increasing accessibility of mail-in voting by mailing applications to all registered voters, RI has suspended their notary/witness requirement for both the primary and general election. However, RI's "emergency" voting process still plays a crucial role in allowing voters who missed the mail-in ballot application deadline, such as those unexpectedly hospitalized in the days leading up to the election, to still cast their ballot. COVID-19 has also forced RI to modify its emergency voting procedures, most notably allowing healthcare workers to serve on bipartisan ballot delivery teams. This commentary highlights these salient updates to voting procedures and serves as a primer as to how interested health care workers may navigate this process alongside patients and lead in the arena of patient voting rights.