Benefits of High-Volume Medical Oncology Care for Noncurable Pancreatic Adenocarcinoma: A Population-Based Analysis
Bibliographic record
Abstract
BACKGROUND: Although pancreatic adenocarcinoma (PA) surgery performed by high-volume (HV) providers yields better outcomes, volume-outcome relationships are unknown for medical oncologists. This study examined variation in practice and outcomes in noncurative management of PA based on medical oncology provider volume. METHODS: This population-based cohort study linked administrative healthcare datasets and included nonresected PA from 2005 through 2016. The volume of PA consultations per medical oncology provider per year was divided into quintiles, with HV providers (≥16 patients/year) constituting the fifth quintile and low-volume (LV) providers the first to fourth quintiles. Outcomes were receipt of chemotherapy and overall survival (OS). The Brown-Forsythe-Levene (BFL) test for equality of variances was performed to assess outcome variability between provider-volume quintiles. Multivariate regression models were used to examine the association between management by HV provider and outcomes. RESULTS: A total of 7,062 patients with noncurable PA consulted with medical oncology providers. Variability was seen in receipt of chemotherapy and median survival based on provider volume (BFL, P<.001 for both), with superior 1-year OS for HV providers (30.1%; 95% CI, 27.7%-32.4%) compared with LV providers (19.7%; 95% CI, 18.5%-20.6%) (P<.001). After adjustment for age at diagnosis, sex, comorbidity burden, rural residence, income, and diagnosis period, HV provider care was independently associated with higher odds of receiving chemotherapy (odds ratio, 1.19; 95% CI, 1.05-1.34) and with superior OS (hazard ratio, 0.79; 95% CI, 0.74-0.84). CONCLUSIONS: Significant variation was seen in noncurative management and outcomes of PA based on provider volume, with management by an HV provider being independently associated with superior OS and higher odds of receiving chemotherapy. This information is important to inform disease care pathways and care organization. Cancer care systems could consider increasing the number of HV providers to 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| 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 | 0.001 | 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".