Evaluation of patient health outcomes of a student-run free clinic in East Harlem.

BACKGROUND: Most United States medical schools have affiliated student-run free clinics, but the quality of services provided in such contexts compared to national metrics is unknown. This study determines whether a student-run, attending-supervised free clinic servicing a low-income and minority race patient population in New York City can meet national metrics of care. METHODS: Through chart review from January 1, 2020 to December 31, 2020, patient outcomes and service utilization in the Healthcare Effectiveness Data and Information Set were examined and compared to national rates of patients using Medicaid HMO or Medicare. Patients are ≥ 21 years of age, residents of East Harlem, and ineligible for health insurance because of legal residency requirements. The majority identify as Hispanic and speak Spanish as their primary language. All patients who were seen in the clinic during the 2020 calendar year were included. The primary study outcome is the number of Healthcare Effectiveness Data and Information Set measures in which patients, seen in a student-run free clinic, meet or exceed national comparisons. RESULTS: The healthcare outcomes of 238 patients, mean age 47.8 years and 54.6% female, were examined in 18 Healthcare Effectiveness Data and Information Set measures. The student-run free clinic met or exceeded national metrics in 16 out of 18 categories. CONCLUSIONS: The student-run free clinic met or exceeded the national standard of care according to national metrics. Evidence-based priorities have been clarified for future improvement. Other student-run free clinics should similarly evaluate the quality of their services.

A foundational transformer leveraging full night, multichannel sleep study data accurately classifies sleep stages.

Study Objectives: To investigate whether a foundational transformer model using 8-hour, multi-channel data from polysomnograms can outperform existing artificial intelligence (AI) methods for sleep stage classification. Methods: We utilized the Sleep Heart Health Study (SHHS) visits 1 and 2 for training and validation and the Multi-Ethnic Study of Atherosclerosis (MESA) for testing of our model. We trained a self-supervised foundational transformer (called PFTSleep) that encodes 8-hour long sleep studies at 125 Hz with 7 signals including brain, movement, cardiac, oxygen, and respiratory channels. These encodings are used as input for training of an additional model to classify sleep stages, without adjusting the weights of the foundational transformer. We compared our results to existing AI methods that did not utilize 8-hour data or the full set of signals but did report evaluation metrics for the SHHS dataset. Results: We trained and validated a model with 8,444 sleep studies with 7 signals including brain, movement, cardiac, oxygen, and respiratory channels and tested on an additional 2,055 studies. In total, we trained and tested 587,944 hours of sleep study signal data. Area under the precision recall curve (AUPRC) scores were 0.82, 0.40, 0.53, 0.75, and 0.82 and area under the receiving operating characteristics curve (AUROC) scores were 0.99, 0.95, 0.96, 0.98, and 0.99 for wake, N1, N2, N3, and REM, respectively, on the SHHS validation set. For MESA, the AUPRC scores were 0.56, 0.16, 0.40, 0.45, and 0.65 and AUROC scores were 0.94, 0.77, 0.87, 0.91, and 0.96, respectively. Our model was compared to the longest context window state-of-the-art model and showed increases in macro evaluation scores, notably sensitivity (3.7% increase) and multi-class REM (3.39% increase) and wake (0.97% increase) F1 scores. Conclusions: Utilizing full night, multi-channel PSG data encodings derived from a foundational transformer improve sleep stage classification over existing methods.

Exposure-Response and Subgroup Analyses to Support Body Weight-Based Dosing of Brentuximab Vedotin in Children and Young Adults with Newly Diagnosed High-risk Classical Hodgkin Lymphoma.

PURPOSE: The purpose of the study was to evaluate the relationships between brentuximab vedotin (BV) pharmacokinetics, age, and body weight (BW) with efficacy and safety in pediatric and young adult patients with previously untreated, high-risk classical Hodgkin lymphoma in the phase III AHOD1331 study. EXPERIMENTAL DESIGN: Overall, 296 patients (age 2-21 years) in the overall population were randomized to and received BV + chemotherapy; the pharmacokinetic subpopulation comprised 24 patients (age <13 years). Age- and/or BW-based (pharmacokinetic surrogates) subgroup analyses of efficacy and safety were conducted for the overall population. Exposure-response analyses were limited to the pharmacokinetic subpopulation. RESULTS: There were no visible trends in disease characteristics across pediatric age subgroups, whereas BW increased with age. Observed antibody-drug conjugate exposures in patients ages <12 years were lower than those in adults administered BV 1.8 mg/kg every 3 weeks, as exposure increased with BW. Nevertheless, no detrimental impact on event-free survival was seen in younger subgroups: 3-year event-free survival rates were 96.2% (2-<12 years) and 92.0% (12-<18 years), with no events observed in those ages <6 years. Neither early response nor lack of need for radiation therapy was associated with high pharmacokinetic exposure. No evidence of exposure-driven grade ≥2 or ≥3 peripheral neuropathy or grade ≥3 neutropenia was seen in exposure-safety and BW-based subgroup analyses; the incidence of these safety events was comparable across pediatric age subgroups, despite lower exposure in younger children. CONCLUSIONS: No further adjustments based on age or BW are required for the BV dosage (1.8 mg/kg every 3 weeks) approved in children.