Morbidity and mortality following major large bowel resection for colorectal cancer detected by a population-based screening program
Bibliographic record
Abstract
BACKGROUND AND AIMS: In 2008, Ontario initiated a population-based colorectal screening program using guaiac fecal occult blood testing. This work was undertaken to fill a major gap in knowledge by estimating serious post-operative complications and mortality following major large bowel resection of colorectal cancer detected by a population-based screening program. METHODS: We identified persons with a first positive fecal occult blood result between 2008 and 2016, at the age of 50-74 years, who underwent a colonoscopy within 6 months, and proceeded to major large bowel resection for colon cancer within 6 months or rectosigmoid/rectal cancer within 12 months, and identified an unscreened cohort of resected cases diagnosed during the same years at the age of 50-74 years. We identified serious postoperative complications and readmissions ≤30 days following resection, and postoperative mortality ≤30 days, and between 31 and 90 days among the screen-detected and the unscreened cohorts. RESULTS: Serious post-operative complications or readmissions within 30 days were observed among 1476/4999 (29.5%) cases in the screen-detected cohort, and among 3060/8848 (34.6%) unscreened cases. Mortality within 30 days was 43/4999 (0.9%) among the screen-detected cohort, and 208/8848 (2.4%) among the unscreened cohort. Among 30 day survivors, mortality between 31 and 90 days was 28/4956 (0.6%) and 111/8640 (1.3%), respectively. CONCLUSION: Serious post-operative complications, readmissions, and mortality may be more common following major large bowel resection for colorectal cancer between the ages of 50 and 74 among unscreened compared to screen-detected cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".