Associations of preoperative anaemia with healthcare resource use and outcomes after colorectal surgery: a population-based cohort study
Bibliographic record
Abstract
Background Preoperative anaemia is common in patient undergoing colorectal surgery. Understanding the population-level costs of preoperative anaemia will inform development and evaluation of anaemia management at health system levels. Methods This was a population-based cohort study using linked, routinely collected data, including residents from Ontario, Canada, aged ≥18 yr who underwent an elective colorectal resection between 2012 and 2022. Primary exposure was preoperative anaemia (haemoglobin <130 g L −1 in males; <120 g L −1 in females). Primary outcome was 30-day costs in 2022 Canadian dollars (CAD), from the perspective of a publicly funded healthcare system. Secondary outcomes included red blood cell transfusion, major adverse events (MAEs), length of stay (LOS), days alive at home (DAH), and readmissions. Results We included 54,286 patients, with mean 65.3 (range 18–102) years of age and 49.0% females, among which 21 264 (39.2%) had preoperative anaemia. There was an absolute adjusted cost increase of $2671 per person at 30 days after surgery attributable to preoperative anaemia (ratio of means [RoM] 1.05, 95% confidence interval [CI] 1.04–1.06). Compared with the control group, 30-day risks of transfusion (odds ratio [OR] 4.34, 95% CI 4.04–4.66), MAEs (OR 1.14, 95% CI 1.03–1.27), LOS (RoM 1.08, 95% CI 1.07–1.10), and readmissions (OR 1.16, 95% CI 1.08–1.24) were higher in the anaemia group, with reduced DAH (RoM 0.95, 95% CI 0.95–0.96). Conclusions Approximately $2671 CAD per person in 30-day health system costs are attributable to preoperative anaemia after colorectal surgery in Ontario, Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".