Volume and process of care in high‐risk cancer surgery
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Bibliographic record
Abstract
BACKGROUND: Although relations between procedure volume and operative mortality are well established for high-risk cancer operations, differences in clinical practice between high-volume and low-volume centers are not well understood. The current study was conducted to examine relations between hospital volume, process of care, and operative mortality in cancer surgery. METHODS: Using the Medicare claims database (2000-2002), we identified all patients undergoing major resections for lung, esophageal, gastric, liver, or pancreatic cancer (n=71,558). Preoperative, intraoperative, and postoperative processes of care potentially related to operative mortality were identified from inpatient, outpatient, and physician claims files using appropriate International Classification of Diseases--Clinical Modification (ICD-9) and Current Procedural Terminology (CPT) codes. We then assessed variation in the use of each process according to hospital volume, adjusting for patient characteristics and procedure type. Study Participants were US Medicare patients. The main outcome measure was specific processes of care. RESULTS: Relative to those at low-volume centers (lowest 20th by volume), patients at high-volume hospitals (highest 20th) were significantly more likely to undergo stress tests (odds ratio [OR]: 1.51, 95% confidence interval [CI]: 1.21-1.87), but not other preoperative imaging tests. They were more likely to see medical or radiation oncologists (OR: 1.37, 95% CI: 1.16-1.62), but not other specialists, preoperatively. Although blood transfusions and use of epidural pain management did not vary significantly by volume, patients at high-volume hospitals had significantly longer operations and were more likely to receive perioperative invasive monitoring (OR: 2.56, 95% CI: 1.82-3.60). Differences in measurable processes of care did not explain volume-related differences in operative mortality to any significant degree. CONCLUSIONS: Although high-volume and low-volume hospitals differ with regard to many aspects of perioperative care, mechanisms underlying volume-outcome relations in high-risk cancer surgery remain to be identified.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it