Prognostic Impact of Resection Margin Status on Distal Pancreatectomy for Ductal Adenocarcinoma
Bibliographic record
Abstract
Pancreatic cancer is associated with a poor prognosis. While surgical resection is the only treatment option with curative intent, most patients die of locoregional and/or distant recurrence. The prognostic impact of the resection margin status has received much attention. However, the evidence is almost exclusively related to pancreatoduodenectomies, while corresponding data for distal pancreatectomy specimens are limited. The key data, such as the rate of microscopic margin involvement ("R1"), the site of margin involvement, and the impact of R1 on patient outcome, are divergent between studies and do not currently allow any general conclusions. The main reasons for the variability in the published data are the small size of the study cohorts and their heterogeneity, as well as the marked divergence in pathology examination practices. The latter is a consequence of the lack of concrete guidance, both for grossing and microscopic examination. The increasing administration of neoadjuvant chemo(radio)therapy introduces a further factor of uncertainty as the conventional definition of a tumour-free margin ("R0") based on 1 mm clearance is inadequate for these specimens. This review discusses the published data regarding the prognostic impact of margin status in distal pancreatectomy specimens along with the challenges and uncertainties that are related to the assessment of the margins.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".