Day of surgery capillary glucose predictability of complications and length of stay for total knee arthroplasty patients with diabetes: a retrospective cohort study
Bibliographic record
Abstract
Background: The aim of this study is to determine if the day of surgery capillary blood glucose readings predict complications and length of stay (LOS) in patients with diabetes undergoing total knee arthroplasty (TKA). Methods: Patients 45 years and above with diabetes who had a primary TKA between April 2015 and April 2019 at The Moncton Hospital were identified using our discharge database. Using patient charts, study variables collected included day of surgery capillary blood glucose, demographic information, Charlson Comorbidity Index, surgery indication, American Society of Anesthesiologists score, diabetes management, complications within 90 days (urinary tract infection, acute kidney injury, wound infection, bleed, venous thromboembolism) and LOS. Results: The area under the receiver operating characteristic curve for a day of surgery capillary glycemia prediction of LOS was 0.578 (95% confidence interval: 0.491-0.664) with a P-value of 0.063, which was not statistically significant. The receiver operating characteristic curve for postoperative complications prediction by day of surgery capillary blood glucose was 0.564 (95% confidence interval: 0.426-0.701) with a P-value of 0.319, which was not statistically significant. Routine preoperative A1C and random glucose were predictive of the day of surgery glucose. Conclusions: Preoperative A1C and random glucose were predictive of the day of surgery capillary glucose in elective TKA surgeries. There was no association between preoperative capillary glucose and complications or LOS. Since current recommendations are variable and largely empiric, there is a need for a randomized-controlled study of preoperative diabetes management, particularly for orthopedic surgeries. This could minimize procedure delays and reduce morbidity and mortality for patients.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".