The impact of COVID-19 on pancreaticoduodenectomy outcomes in a hepatopancreatobiliary centre of excellence
Bibliographic record
Abstract
Background At the beginning of the COVID-19 pandemic, access to “planned” surgical care was restricted as the health care system responded to the coronavirus. We hypothesized that the pandemic resulted in diagnostic and therapeutic delays, leading to stage migration among patients with malignancies treated with a Whipple procedure. Methods This study is a retrospective review of adults who underwent surgical exploration for a planned pancreaticoduodenectomy for malignancy at St. Joseph’s Health Centre between March 11, 2019, and March 11, 2021. Results We included 180 patients in the study. Baseline characteristics, pathologic diagnoses, and perioperative outcomes were similar between the 2 cohorts. The post-COVID group had longer median wait times from date of consent (p < 0.001), and from computed tomography (CT) scan (p < 0.001), to surgery. There were increased rates of R1 margin positivity in the post-COVID group (p = 0.01). We saw an association between higher wait times from consent and the last CT scan to the date of operation, and increased rates of R1 margin positivity in the first year of the pandemic. Conclusion This study demonstrated the importance of prioritizing care during a pandemic and provided evidence for potential long-term consequences when there are delays in surgery for aggressive gastrointestinal malignancies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".