P6064Steroids in cardiac surgery (SIRS): infection substudy
Bibliographic record
Abstract
Background: Infections following cardiac surgery result in significant morbidity, mortality and healthcare cost. To target populations for prophylactic interventions, clinicians are interested in predictors of post-operative infections. Methods: Steroids in Cardiac Surgery (SIRS) was a multi-centre randomized controlled trial assessing the intraoperative use of methylprednisone during cardiac surgery. 7507 patients were enrolled in 80 centers and 18 countries. Using the participants as a cohort, we aimed to identify independent risk factors for post-operative wound infections. We excluded those who did not undergo surgery, died intraoperatively or within 48 hours of operation. Patients were identified as having developed “surgical site infection” or not by postoperative day 30. Using hypothesized and known risk factors, we created a binary logistic regression model using a forward step-wise entry model. Results: Follow-up at 30 days was complete for all patients; 7406 were included in the cohort. Risk factors significant at the p<0.05 level include: diabetes managed with insulin (aOR: 1.53, 95% CI: 1.12–2.10), oral hypoglycemics (1.58, 1.16–2.13), or diet (1.66, 1.05–2.62), female gender (1.32, 1.04–1.70), renal failure with (2.05, 1.07–3.95), and without (1.52, 1.06–2.17) dialysis, >96 minutes cardiopulmonary bypass (CPB) time (1.86, 1.45–2.38), BMI >30.49 (1.56, 1.22–1.99), peak ICU blood-sugar (mmol/L) (1.02, 1.00–1.04), dual-antiplatelet therapy (1.44, 1.01–2.05), CABG operation type (2.55, 1.84–3.54).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".