Association of Elevated Pre‐operative Hemoglobin A1c and Post‐operative Complications in Non‐diabetic Patients: A Systematic Review
Bibliographic record
Abstract
IMPORTANCE: Pre-operative hyperglycemia is associated with post-operative adverse outcomes in diabetic and non-diabetic patients. Current pre-operative screening includes random plasma glucose, yet plasma glycated hemoglobin (HbA1c) is a better measure of long-term glycemic control. It is not clear whether pre-operative HbA1c can identify non-diabetic patients at risk of post-operative complications. OBJECTIVE: The systematic review summarizes the evidence pertaining to the association of suboptimal pre-operative HbA1c on post-operative outcomes in adult surgical patients with no history of diabetes mellitus. EVIDENCE REVIEW: A detailed search strategy was developed by a librarian to identify all the relevant studies to date from the major online databases. FINDINGS: Six observational studies met all the eligibility criteria and were included in the review. Four studies reported a significant association between pre-operative HbA1c levels and post-operative complications in non-diabetic patients. Two studies reported increased post-operative infection rates, and two reported no difference. Of four studies assessing the length of stay, three did not observe any association with HbA1c level and only one study observed a significant impact. Only one study found higher mortality rates in patients with suboptimal HbA1c. CONCLUSIONS AND RELEVANCE: Based on the limited available evidence, suboptimal pre-operative HbA1c levels in patients with no prior history of diabetes predict post-operative complications and represent a potentially modifiable risk factor.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".