Understanding the hospital safety net
Bibliographic record
Abstract
INTRODUCTION: Safety net hospitals (SNHs) care for a substantial population of vulnerable patients and are often resource-limited. These limitations may impact treatment decisions for high-risk prostate cancer (hPCa). We performed the first population-based analysis examining SNH status and treatment decisions for localized hPCa. METHODS: percentile of Medicaid and uninsured caseload. Non-curative-intent treatment was defined as androgen deprivation monotherapy (ADT) or no treatment. Outcomes assessed were treatment choice and overall survival (OS) by SNH status. RESULTS: A total of 95 747 patients with hPCa were included; 112 hospitals were identified as SNHs, with mean Medicaid/uninsured caseload of 24.4% compared to 3.2% at non-SNHs (p<0.01). Patients at SNHs were independently associated with greater odds of non-curative-intent treatment (odds ratio [OR] 2.2, p<0.01). Results were consistent across subgroups: private insurance (OR 2.2, p<0.01), age <65 (OR 2.3, p<0.01), and at academic centers (OR 1.9, p<0.01). There was no difference in OS among SNHs and non-SNHs when patients received curative treatment. Among patients who did not receive curative treatment, OS was greater at SNHs (hazard ratio 0.82, p=0.02). CONCLUSIONS: Patients at SNHs were more likely to receive non-curative treatment independent of known socioeconomic risk factors. Private insurance or treatment at academic centers did not mitigate these disparities. Increased resources may be needed at SNHs, especially in the context of healthcare expansion, which may further strain these facilities.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Health services study of prostate cancer treatment at safety net hospitals.
The study examines hospital treatment disparities for prostate cancer, not research itself.
Health-services study of prostate-cancer treatment at safety-net hospitals, clinical care not research.
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".