Adjuvant treatment in resected pancreatic adenocarcinoma: A retrospective analysis of survival and prognostic factors in 141 patients
Bibliographic record
Abstract
15104 Background: Resectable pancreatic adenocarcinoma (PA) remains highly lethal. It is debatable whether adjuvant therapy can improve prognosis. Furthermore, what treatment should be delivered and for whom it is indicated is still of controversy. Methods: Between 1992–2004, retrospective data from the 141 patients who had a PA resected with curative intent in our centres were reviewed. Adjuvant treatment was as follows: 41 had chemoradiation (CRT), 1 neoadjuvant CRT, 10 chemotherapy alone (CT), 1 radiotherapy alone, and for 7 adjuvant treatment is unknown. The other 81 patients were observed. Overall survival (OS), relapse-free survival (RFS), locoregional and distant failure-free survival (LRFFS and DFFS) were analysed according to published prognostic factors. Kaplan-Meier curves with log-rank tests and Chi-2 coefficients were computed for the analysis. Results: Median age at diagnosis was 61 years. Overall, 63% of the patients were node positive, 67% had lymphovascular invasion (LVI), 71% had elevated CA-19.9 values and 50% operative blood loss >550cc. Table 1 displays these prognostics factors by treatment groups. For the entire cohort, median OS was 16.9 months; 1-year and 3-year OS were 76% and 43% respectively. No influence of treatment arm was found on OS (p=0.71). Negative nodes, normal CA-19.9 values, absence of LVI and blood loss <=550cc had all a positive influence on OS (all p<0.05). RFS and DFFS were not affected by adjuvant treatment (p=0.61 and 0.68, resp.), but a lower LRFFS was associated with it (p=0.006). Conclusions: In this retrospective analysis, despite the imbalance of prognostic factors among treatment groups, toward more ill patients in CT and CRT arms, there is no difference in OS, DFS and DFFS between arms. Therefore, adjuvant treatment seems to counterbalance the effect of poor prognostic factors. A randomized prospective trial would be needed to further address the issue. [Table: see text] No significant financial relationships to disclose.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".