The prognostic utility of galectin-3 in patients undergoing cardiac surgery: a scoping review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To review the utility of galectin-3 (Gal-3) as a biomarker for postoperative adverse outcomes in patients undergoing cardiac surgery. METHOD: This review was conducted in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Electronic database search was conducted in October 2023. Studies that measured pre- and/or postoperative plasma Gal-3 levels in adult patients undergoing cardiac surgery were included. Primary outcomes included postoperative morbidity and mortality. RESULTS: Out of 391 studies screened, eight studies met the inclusion criteria. Two of the three studies showed that preoperative plasma levels of Gal-3 were associated with acute kidney injury (AKI) after cardiac surgery. Two of the three studies reported a significant increase in preoperative Gal-3 levels in patients who developed postoperative atrial fibrillation (POAF). The addition of Gal-3 to the EuroSCORE II model was found to statistically improve the prediction of both AKI and POAF. Three of the five studies suggested that Gal-3 levels can predict postoperative mortality. Finally, one study suggested that lower preoperative Gal-3 levels was associated with a higher likelihood of achieving left ventricular reverse remodeling (LVRR) after surgery. CONCLUSIONS: Gal-3 may play a promising role in predicting adverse outcomes in patients undergoing cardiac surgery. The addition of Gal-3 to clinical risk prediction scores may improve their discriminatory power in this group of patients. Future studies are warranted to justify its incorporation into routine clinical practice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.000 |
| 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 it