Day-of-surgery quality gaps in glycemic management: a retrospective cohort study
Bibliographic record
Abstract
Perioperative hyperglycemia is associated with worse patient outcomes. Characterizing quality gaps in day-of-surgery glucose management can guide quality improvement teams to address this risk factor for infection, readmission, and death. This retrospective cohort study used administrative and electronic health record data to describe process, outcome, and balancing measures of day-of-surgery glycemic management for adult patients with and without diabetes undergoing surgery at 6 hospitals in Alberta, Canada between 2019 and 2024. Participants were stratified by diabetes, prediabetes, no diabetes and unknown diabetes status. We report the association between hyperglycemia (blood glucose ≥ 10.0 mmol.L −1 ) and length of stay, admission to ICU, and 30-day readmissions as an exploratory analysis. There were 12,275 eligible procedures including 3,164 procedures performed on patients with diabetes (25.8%). Of patients with diabetes, 85.4% ( n = 2,703) had at least one glucose measurement on the day of surgery and 37.1% ( n = 1,004) had hyperglycemia. About half of patients with diabetes and hyperglycemia received insulin (51.4%, n = 516). More than 10% of patients with prediabetes, no diabetes, and unknown diabetes status had hyperglycemia and less than 20% received insulin. Patients with hyperglycemia on the day of surgery had longer length of stay (4.49 days; 95% CI 4.70 to 5.18 days; p < 0.0001), postoperative ICU admission (aOR 5.37; 95% CI 4.45–6.49; p < 0.001) and odds of 30-day readmission (aOR 2.19; 95% CI 1.89–2.54; p < 0.0001). There were important quality gaps in glucose measurement and hyperglycemia treatment for patients with diabetes. Hyperglycemia was common and clinically significant among patients without diabetes. Future work to understand the prevalence of hyperglycemia in patients without diabetes and to address quality gaps in day-of-surgery glucose measurement are needed.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".