Factors affecting the administration of adjuvant therapy in patients with pancreatic adenocarcinoma.
Bibliographic record
Abstract
e15087 Background: Adjuvant therapy for pancreatic adenocarcinoma is now considered standard of care. The proportion of patients receiving adjuvant therapy (ADT) following pancreatic resection is a good quality indicator of cancer care. The aim of this study was to evaluate factors associated with receiving ADT in patients with pancreatic cancer. Methods: Between years 2000-2010, all patients undergoing pancreaticoduodenectomy for pancreatic adenocarcinoma at a single high-volume hepatotopancreatobiliary center were evaluated. The impact of demographic, peri-operative and pathological risk factors affecting the administration of ADT were analyzed using univariate and multivariate logistic regression analysis. Results: There were 258 patients identified. Median age was 65 (37-84) years, 54% were females. There was a 15% margin positivity rate, 14% pancreatic leak rate, 14.7% major complication rate, and 1.2% 90 day/in-hospital mortality rate. Overall, 160/258 (70%) of patients received adjuvant therapy. On multivariate analysis; age, presence of major complications, node-negative disease and earlier era (2000-2004) were significantly associated with a lower probability of receiving ADT. Reasons for not receiving ADT were; patient preference: 20/67 (32%), not recommended: 14/67 (23%), disease recurrence: 12/67 (9.5%) and being medically unfit for ADT: 18/67 (11.5%). None of these reasons were different between time-periods except for fewer patients being offered ADT from 2000-2005 (15.4% vs. 2.5%, p <0.001). Conclusions: Thirty percent of patients do not receive ADT following pancreatectomy. Those with advanced age; node-negative disease and those who had major complications after pancreaticoduodenectomy were less likely to receive ADT. The impact of these factors should be taken into account when considering the administration of ADT.
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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 | 0.004 |
| 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) | 0.002 | 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".