Seasonal variations in pancreatic surgery outcome A retrospective time-trend analysis of 2748 Whipple procedures
Bibliographic record
Abstract
BACKGROUND: Observing cyclic patterns in surgical outcome is a common experience. We aimed to measure this phenomenon and to hypothesize possible causes using the experience of a high-volume pancreatic surgery department. METHODS: Outcomes of 2748 patients who underwent a Whipple procedure at a single high-volume center from January 2000 to December 2018 were retrospectively analyzed. Three different hypotheses were tested: the effect of climate changes, the "July effect" and the effect of vacations. RESULTS: Clavien-Dindo ≥ 3 morbidity was similar during warm vs. cold months (22.5% vs. 19.8%, p = 0.104) and at the beginning of activity of new trainees vs. the rest of the year (23.5 vs. 22.5%, p = 0.757). Patients operated when a high percentage of staff is on vacation showed an increased Clavien-Dindo ≥ 3 morbidity (22.3 vs. 18.5%, p = 0.022), but similar mortality (2.3 vs. 1.8%, p = 0.553). The surgical waiting list was also significantly longer during these periods (37 vs. 27 days, p = 0.037). Being operated in such a period of the year was an independent predictor of severe morbidity (OR 1.271, CI 95% 1.086-1.638, p = 0.031). CONCLUSION: Being operated when more staff is on vacation significantly affects severe morbidity rate. Future healthcare system policies should prevent the relative shortage of resources during these periods.
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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 | 0.001 | 0.002 |
| 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".