Effect of preoperative cognitive dysfunction on postoperative outcomes in cardiac surgery
Bibliographic record
Abstract
Background: Preoperative cognitive dysfunction (PreOCD) has not been well described as a risk factor forpoor outcomes in cardiac surgery. This study was aimed to demonstrate the effects of PreOCD on postoperative outcomes in cardiac surgery.Methods: One hundred adult patients undergoing elective open cardiac surgery at King ChulalongkornMemorial Hospital were recruited. The Montreal Cognitive Assessment (MoCA) was used to evaluate cognitivefunction the evening before surgery and defined a score of less than 26 as cognitive impairment. We compared postoperative outcome data between patients with PreOCD group and no PreOCD group.Results: One patient in PreOCD group withdrew from our study. Sixty nine out of 99 patients (68.31%) had PreOCD. In the PreOCD group, the postoperative mechanical ventilation period was significantly longer (15.9 gif.latex?\pm 26.6 vs. 7.4 gif.latex?\pm 8.76 hours, gif.latex?\rho = 0.018), ICU stay was significantly longer (39.9 gif.latex?\pm 43.7 vs. 27.6 gif.latex?\pm 15.0 hours, gif.latex?\rho = 0.039), and cost of hospital stay was significantly higher (13,540 gif.latex?\pm 6,355 vs. 11,264 gif.latex?\pm 5,229 baht, gif.latex?\rho = 0.014). However, the length of hospital stay was not significantly different (10.1 gif.latex?\pm 3.8 vs. 9.3 gif.latex?\pm 4.4 days, gif.latex?\rho = 0.384).Conclusions: We demonstrated the adverse effects of PreOCD on patients outcomes after cardiac surgery.Identifying PreOCD may be useful in risk stratification during preoperative assessment to improve surgicaloutcomes.
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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 | 0.005 |
| 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 | 0.000 |
| 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".