Clinical factors associated with arrhythmia and short-term prognosis following mitral valve repair: a retrospective cohort study
Bibliographic record
Abstract
Background: Postoperative arrhythmia (POA) is one of the common and serious postoperative complications. This retrospective study was conducted to investigate the clinical factors associated with POA and its short-term prognosis following mitral valve repair. Methods: A total of 618 patients receiving mitral valve repair between January 2015 and November 2020 in our hospital were included in this retrospective study, including 318 males and 300 females and aged 53.9±9.3 years. The patients were grouped into arrhythmia and non-arrhythmia groups and investigated for risk factors associated with the prognosis of POA using multivariate logistic regression based on their clinical data. Results: POA was observed in 314 (50.8%) patients and atrial fibrillation (AF) was the most frequent (43.3%) type of POA. Compared with non-arrhythmia patients, arrhythmia patients had significantly longer time to use vasoactive drug use, longer intensive care unit (ICU) stay and longer hospital stay. In addition, the incidence of postoperative heart failure was significantly higher (P<0.05). Logistic regression analysis showed that preoperative arrhythmia [odds ratio (OR) =9.17; 95% confident interval (CI): 4.49-18.10], postoperative pain (OR =4.70; 95% CI: 1.55-6.12) and postoperative hypoxemia (OR =3.25; 95% CI: 1.04-6.28) were independently associated with POA. Conclusions: This study demonstrates that the incidence of arrhythmia is relatively high after mitral valve repair and is associated with preoperative arrhythmia, postoperative pain and postoperative hypoxemia.
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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 | 0.002 |
| 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 | 0.001 |
| 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".