Predictors of Adjuvant Treatment for Pancreatic Adenocarcinoma at the Population Level
Bibliographic record
Abstract
Background: In the present study, we aimed to describe, at the population level, patterns of adjuvant treatment use after curative-intent resection for pancreatic adenocarcinoma (PCC) and to identify independent predictors of adjuvant treatment use. Methods: In this observational cohort study, patients undergoing PCC resection in the province of Ontario (population 13 million) during 2005–2010 were identified using the provincial cancer registry and were linked to administrative databases that include all treatments received and outcomes experienced in the province. Patients were defined as having received chemotherapy (CTX), chemoradiation (CRT), or observation (OBS). Clinicopathologic factors associated with the use of CTX, CRT, or OBS were identified by chi-square test. Logistic regression analyses were used to identify independent predictors of adjuvant treatment versus OBS, and CTX versus CRT. Results: Of the 397 patients included, 75.3% received adjuvant treatment (27.2% CRT, 48.1% CTX) and 24.7% received obs. Within a single-payer health care system with universal coverage of costs for CTX and CRT, substantial variation by geographic region was observed. Although the likelihood of receiving adjuvant treatment increased from 2005 to 2010 (p = 0.002), multivariate analysis revealed widespread variation between the treating hospitals (p = 0.001), and even between high-volume hepatopancreatobiliary hospitals (p = 0.0006). Younger age, positive lymph nodes, and positive surgical resection margins predicted an increased likelihood of receiving adjuvant treatment. Among patients receiving adjuvant treatment, positive margins and a low comorbidity burden were associated with CRT compared with CTX. Conclusions: Interinstitutional medical practice variation contributes significantly to differential patterns in the rate of adjuvant treatment for PCC. Whether such variation is warranted or unwarranted requires further investigation.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".