Economic impacts of care by high-volume providers for noncurative esophagogastric cancer: A population-based analysis.
Bibliographic record
Abstract
339 Background: Esophagogastric cancer (EGC) is one of the deadliest and costliest malignancies to treat. Care by high-volume providers can provide better outcomes for patients with EGC. Cost implications of volume-based cancer care are unclear. We examined the cost-effectiveness of care by high-volume medical oncology providers for non-curative management of EGC. Methods: We conducted a population-based cohort study of non-curative EGC over 2005-2017 by linking administrative healthcare datasets. High-volume was defined as >11 patients/provider/year. Healthcare costs ($USD/patient/month-survived) were computed from diagnosis to death or end of follow-up from the perspective of the healthcare system using validated costing algorithms. Multivariable quantile regression examined the association between care by high-volume providers and costs. Sensitivity analyses were conducted by varying costing horizons and high-volume definitions. Results: Among 7,011 non-curative EGC patients, median overall survival was superior with care by high-volume providers with 7.0 (IQR: 3.3-13.3) compared to 5.9 (IQR: 2.6-12.1) months (p < 0.001) for low-volume providers. Median costs/patient/month-lived were lower for high-volume providers ($5,518 vs. $5,911; p < 0.001), owing to lower inpatient acute care costs, despite higher medication-associated and radiotherapy costs. Care by high-volume providers was independently associated with a reduction of $599 per patient/month-lived (95% confidence interval: -966 to -331) compared to low-volume providers. The incremental cost-effectiveness ratio was -393. Care by high-volume providers remained the dominant strategy when varying the high-volume definition and the costing time horizon. Conclusions: Care by high-volume providers for non-curative EGC is associated with superior survival and lower healthcare costs, indicating a dominant strategy that may provide an opportunity to improve cost-effectiveness of care delivery.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".