Early Recurrence after Upfront Surgery for Pancreatic Ductal Adenocarcinoma
Bibliographic record
Abstract
Background. Survival after surgery for pancreatic ductal adenocarcinoma (PDAC) remains poor, due to early recurrence (ER) of the disease. A global definition of ER is lacking and different cut-off values (6, 8, and 12 months) have been adopted. The aims of this study were to define the optimal cut-off for the definition of ER and predictive factors for ER. Methods. Recurrence was recorded for all consecutive patients undergoing upfront surgery for PDAC at our institute between 2010 and 2017. Receiver operating characteristic (ROC) curves were utilized, to estimate the optimal cut-off for the definition of ER as a predictive factor for poor post-progression survival (PPS). To identify predictive factors of ER, univariable and multivariable logistic regression models were used. Results. Three hundred and fifty one cases were retrospectively evaluated. The recurrence rate was 76.9%. ER rates were 29.0%, 37.6%, and 47.6%, when adopting 6, 8, and 12 months as cut-offs, respectively. A significant difference in median PPS was only shown between ER and late recurrence using 12 months as cut-off (p = 0.005). In the multivariate analysis, a pre-operative value of CA 19-9 > 70.5 UI/L (OR 3.10 (1.41–6.81); p = 0.005) and the omission of adjuvant treatment (OR 0.18 (0.08–0.41); p < 0.001) were significant predictive factors of ER. Conclusions. A twelve-months cut-off should be adopted for the definition of ER. Almost 50% of upfront-resected patients presented ER, and it significantly affected the prognosis. A high preoperative value of CA 19-9 and the omission of adjuvant treatment were the only predictive factors for ER.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".