Resection to Exploration Ratios and Associated Outcomes in Patients with Pancreatic Ductal Adenocarcinoma
Bibliographic record
Abstract
OBJECTIVE: To investigate resection/exploration ratios (RERs), reasons for omission of pancreatectomy, and survival outcomes in patients undergoing surgical exploration with resection intent for pancreatic ductal adenocarcinoma (PDAC). BACKGROUND: While surgical indications for PDAC are expanding, information about intraoperative attrition is lacking. METHODS: The RER was calculated in patients with PDAC undergoing exploration from 2018 through 2020. Factors associated with uncompleted resection and survival were identified using multivariable models. RESULTS: In total, 681 patients were included. Upfront explorations were 296 (43.7%), and postneoadjuvant explorations were 385 (56.3%). The overall RER was 89.7% (90.5% in the upfront setting and 89.1% postneoadjuvant treatment). In this latter subgroup, the RER decreased from 96.1% in resectable disease to 86.6% in borderline resectable disease and 61.9% in locally advanced disease. The primary reasons for uncompleted resection were occult metastases in presumed resectable/borderline resectable disease (without difference between upfront and postneoadjuvant operations) and local unresectability in locally advanced disease. No preoperative variable was associated with uncompleted resection in upfront explorations, while anatomic staging informed the likelihood of surgical attrition following neoadjuvant treatment. Uncompleted resection was invariably associated with a median survival of around 1 year. The median postpancreatectomy survival was 36.9 months in the upfront setting and 29.5 months following neoadjuvant treatment. The median survival from diagnosis in patients receiving postneoadjuvant resection was 34.5 months. CONCLUSIONS: This analysis provided contemporary information about resection rates, reasons for intraoperative attrition, and survival outcomes in the entire spectrum of patients with PDAC selected for surgical exploration at an experienced institution.
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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 | 0.003 |
| 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".