Extended delay in endoscopic mucosal resection is not associated with adverse outcomes: Findings from the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Background and study aims The aim of this study was to investigate the impact of delayed endoscopic mucosal resection (EMR) of colorectal polyps on health outcomes. Patients and methods A bidirectional cohort study was completed. A baseline group consisting of all EMRs performed within a 15-month period before a province-wide, government-mandated cessation of EMR procedures due to the global pandemic was compared to EMRs impacted by the shutdown, defined as the COVID-19-delayed group. The primary outcome was the incidence of malignant polyps. Secondary outcomes included technical success, polyp recurrence at follow-up colonoscopy, advanced polyp histology, probability of meeting endoscopic criteria for adequate resection for malignant polyps, metastatic colorectal cancer, and complications. Results A total of 268 EMR procedures were included in the study cohort, of which 208 formed the baseline group and 60 were in the COVID-19-delayed group. The median (IQR) patient age was 72 (13.0) and 113 (41.2 %) were females. The median (IQR) wait time was 92 days (87.8) in the baseline group and 191 days (127.8) in the COVID-19-delayed group (P < 0.001). Overall, there were no significant differences in the incidence of malignant polyps, technical success, polyp recurrence on follow-up colonoscopy, advanced polyp histology, adequate endoscopic resection for malignant polyps, metastatic colorectal cancer, or complications between the two groups (P > 0.05 for all outcomes). Conclusions A longer wait time for EMR of colorectal polyps, increasing from a median of 92 to 191 days, was not associated with worse outcomes.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".