Quality gaps in screening and monitoring for postoperative hyperglycemia in a Canadian hospital: a retrospective cohort study
Bibliographic record
Abstract
INTRODUCTION: Evidence-based preoperative, intraoperative and postoperative glycemic management may reduce poor surgical outcomes. Previous studies suggest that quality gaps in perioperative glycemic management may be common. RESEARCH DESIGN AND METHODS: This retrospective cohort study used administrative health and laboratory data from a single center to estimate quality gaps in perioperative glycemic management in patients with and without diabetes between April 2019 and March 2020. We examined the proportion of patients with preoperative hemoglobin A1c (HbA1c) measurement, postoperative point-of-care testing (POCT) for glucose, hyperglycemia, and basal bolus insulin regimens. We compared the median length of stay (LOS) in patients with and without postoperative hyperglycemia, adjusted for age and sex. RESULTS: There were 6576 patients in our cohort; 1165 (17.8%) had diabetes. Most patients with diabetes had an HbA1c measured prior to surgery (n=697, 59.8%). Postoperatively, 16.9% of patients with diabetes had no POCT monitoring (n=197) and 65.7% had hyperglycemia (n=636). Only 35.9% of patients who received insulin had a basal bolus insulin regimen (n=229). Patients with diabetes who had postoperative hyperglycemia had a longer median LOS compared with those who did not have postoperative hyperglycemia (8.4 days (95% CI 7.5 to 9.4) and 6.7 days (95% CI 6.3 to 7.1), respectively). In patients without diabetes, median LOS was 7.4 days (95% CI 4.4 to 10.4) for those with hyperglycemia and 5.2 days (95% CI 5.1 to 5.4) for those with in-target glucose. CONCLUSIONS: Quality gaps in perioperative glycemic management include measurement of blood glucose after surgery and treatment of postoperative hyperglycemia. These gaps may contribute to longer LOS.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".