Differences in Surgical Cancer Care Delivery and Outcomes Between Safety‐Net and Non‐Safety‐Net Hospitals in the United States: A Comprehensive Systematic Review
Bibliographic record
Abstract
BACKGROUND: Studies evaluating the association of safety-net hospitals (SNHs) with outcomes of surgical care in cancer patients have demonstrated mixed results. We sought to systematically investigate the association of SNH status with measures of surgical cancer care delivery and outcomes. METHODS: A comprehensive review of the literature identified from the MEDLINE/PubMed, Embase, Web of Science, and Cochrane databases was performed according to the PRISMA guidelines. Risk of bias assessment was conducted using the Joanna Briggs Institute's tool. The findings were synthesized qualitatively. RESULTS: Of the 1749 records identified, 33 retrospective studies were included, 79% of which investigated national databases. Risk of bias assessment revealed average score of 78%. Among studies assessing each outcome, lower likelihood of receiving appropriate surgical interventions in SNH patients was reported by 85%; longer intervals to surgery by 100%; and prolonged hospital stays by 73%. Most studies reported no differences in survival (65%) or readmission (67%). Results were mixed regarding complications and mortality. Patient characteristics and shortage of resources and interdisciplinary teams were frequently proposed factors for observed disparities. CONCLUSIONS: Cancer patients at SNHs may be less likely to undergo some surgical treatments and experience longer intervals to treatment but achieve largely comparable short- and long-term outcomes to non-SNH patients.
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How this classification was reachedexpand
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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".