Provider-volume associated with variable receipt of therapy and outcomes for noncurative pancreas adenocarcinoma: A population-based analysis.
Bibliographic record
Abstract
352 Background: While high-volume providers for pancreatic adenocarcinoma (PA) surgery yield better outcomes, variation in practice and the role of provider-volume has not been investigated for systemic therapy. We examined variation in practice and outcomes in the management of non-curative PA, based on medical oncology provider-volume. Methods: We conducted a population based retrospective cohort study of non-resected PA over 2005-2016 by linking administrative healthcare datasets. High-volume (HV) medical-oncology providers were defined as the 5th quintile of number of PA seen per provider per year. Outcomes were receipt of chemotherapy and overall survival (OS). Brown Forsythe Levene (BFL) test for equality of variances assessed outcomes variability between provider-volume quintiles (Q1 to 5). Multivariate regressions examined the association between management by HV provider and receipt of systemic therapy and OS. Results: Of 10,881 non-curative PA patients, 7,062 consulted with medical oncology. Among 341 medical oncology providers, 3% were HV, defined as > 16 patients/year. There was variability in receipt of chemotherapy based on provider-volume, with 44% (IQR: 25-54) for Q1 and 47% (IQR: 43-54) for Q5, and in median survival, with 4.1 months (IQR: 2.7-6.2) for Q1 and 7.5 months (IQR: 6.6-8.0) for Q5. Variability between provider-volume quintiles was significant for receipt of chemotherapy and median survival (both BFL 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 1.19 [1.05-1.34]), and superior OS (HR 0.79 [0.74-0.84]). Conclusions: There was significant variation in non-curative management and outcomes of PA based on provider-volume. Management 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. Cancer care systems could consider initiatives to increase 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 | 0.006 |
| 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".