590 VARIATION IN RECEIPT OF THERAPY AND SURVIVAL WITH PROVIDER-VOLUME IN NON-CURATIVE ESOPHAGO-GASTRIC CANCER: A POPULATION-BASED ANALYSIS
Bibliographic record
Abstract
Abstract While surgical care by high-volume providers for esophago-gastric cancer (EGC) yields better outcomes, volume-outcome relationships are unknown for systemic therapy. We examined receipt of therapy and outcomes in the non-curative management of EGC based on medical oncology provider-volume. Methods We conducted a population based retrospective cohort study of non-curative EGC over 2005–2017 by linking administrative healthcare datasets. The volume of new EGC consultations per medical oncology provider per year was calculated and divided into quintiles. High-volume (HV) providers were defined as the 4-5th quintiles. Outcomes were receipt of chemotherapy and overall survival (OS). Multivariate logistic and Cox-proportional hazards regressions examined the association between management by HV provider, receipt of systemic therapy, and OS. Results 7,011 EGC patients with non-curative management consulted with medical oncology. One-year OS was superior for HV providers (>11 patients/year), with 28.4% (95%CI: 26.7–30.2%) compared to 25.1% (95%CI: 23.8–26.3%) for low-volume (p < 0.001). After adjusting for age, sex, comorbidity burden, rurality, income quintile, and diagnosis year, HV provider was independently associated with higher odds of receiving chemotherapy (OR 1.13, 95%CI 1.01–1.26), and independently associated with superior OS (HR 0.89, 95%CI 0.84–0.93). Conclusion Medical oncology provider-volume was associated with variation in non-curative management and outcomes of EGC. Care by a HV provider was independently associated with higher odds of receiving chemotherapy and superior OS, after adjusting for case-mix. This information is important to inform disease care pathways and care organization; increase in the number of HV providers may reduce variation and improve outcomes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".