Association of Perioperative Glycemic Control With Deep Postoperative Infection After Shoulder Arthroplasty in Patients With Diabetes
Bibliographic record
Abstract
BACKGROUND: Although diabetes mellitus (DM) has been established as a risk factor for infection after lower extremity arthroplasty, no association has been reported with shoulder arthroplasty. METHODS: We queried a national database for patients with DM who underwent primary shoulder arthroplasty. We identified perioperative hemoglobin A1c (HbA1c) levels and assessed the incidence of wound complications within 6 months and deep infection that required surgical intervention within 1 year for each HbA1c level. We performed a receiver operating characteristic analysis to determine a threshold value of HbA1c. RESULTS: Patients with DM had markedly higher rates of wound complications (1.4% versus 0.9%; odds ratio, 1.22; P = 0.028) and deep infection (0.7% versus 0.4%; odds ratio, 1.47; P = 0.001). The rates of wound complications (P = 0.0008) and deep postoperative infection (P = 0.002) increased markedly as the perioperative HbA1c level increased. Receiver operating characteristic analysis demonstrated an inflection point at an HbA1c level of 8.0 mg/dL (P = 0.017; sensitivity, 50%; specificity, 75%). CONCLUSIONS: The risk of wound complications and deep postoperative infection in patients with DM increases as the perioperative HbA1c level increases. An HbA1c level >8.0 mg/dL could serve as a threshold for a markedly increased risk of infection. LEVEL OF EVIDENCE: Therapeutic level III, case-control study, treatment study.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".