Resected Pancreatic Ductal Adenocarcinoma: understanding tumour tropism to maximise benefit from surgery.
Bibliographic record
Abstract
Introduction Relapse-rate in pancreatic ductal adenocarcinoma (PDAC) remains high. Identification of modifiable factors associated with relapse could improve patient selection for surgery. Methods All consecutive patients diagnosed with PDAC undergoing curative surgery between Jan’05 and Sep’17 were retrospectively analysed. Recurrence-Free Survival (RFS)/Overall Survival (OS) were estimated with Kaplan-Meier method and survival analysis performed with univariate/multivariable Cox-regression (Cox). Logistic-regression (LR) was used for identification of risk factors of tumour recurrence. Results One-hundred-eighty-two patients eligible: microscopically involved resection-margins (R1) 65.7%; adjuvant chemotherapy (adj) 62.1%; 78.6% relapsed. Median (months) RFS and OS were 11.4 (95%CI=9.4-13.7) and 21.6 (95%CI=17.9-18.9), respectivelly. Relapse patterns identified included: “local-only” 30.1%, “distant-only” 40.5%, “combined” 29.4%; overall, distant metastases were identified in 69.9% of patients; distant metastases were located mainly in the liver (41.3%) with a median time-to-liver recurrence of 6.64 months (95%CI 4.99-8.56)). Factors impacting on risk of relapse were: R1 [(any-pattern) (LR-multivariable: OR=4.02; 95%CI=0.02-0.23)], pre-adj CA19.9>normal limit (NL) [(‘local-only’) (LR-univariate: OR=0.23; 95%CI=0.08-0.62)] and adj [(‘combined’) (LR-univariate: OR=0.46; 95%CI=0.22-0.96)]. R1 associated with shorter OS (Cox-multivariable: OR=1.90; 95%CI=1.13-3.19) while pre-adj CA19.9>LN implied shorter RFS (Cox-multivariable: OR=2.28; 95%CI=1.38-3.76) and OS (Cox-multivariable: OR=1.84; 95%CI=1.08-3.14). Preoperative magnetic resonance imaging (MRI) liver was associated with a lower risk of relapse [(any pattern) (LR-multivariable: OR=0.06; 95%CI=0.02-0.23)] and was prognostic for longer OS (LR-multivariable: OR=0.27; 95%CI=0.09-0.74). Conclusion Majority of resected-PDAC patients will recur with distant metastases (liver); integrating preoperative MRI liver to patients’ pathway may improve patient selection and maximise benefit from surgery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".