Influence Factors for Cardiovascular Failure Following Successful Cardiopulmonary Resuscitation
Bibliographic record
Abstract
Objective To explore the influence factors for cardiovascular failure following successful cardiopulmonary resuscitation to provide a theoretical basis for the patients' prognosis. Methods The patients with cardiovascular failure following resumption of spontaneous circulation( ROSC) were enrolled in cardiovascular failure group,and the patients without cardiovascular failure following ROSC were enrolled in non-cardiovascular failure group.The data of patient's age,gender,history of cardiovascular disease,cardiopulmonary resuscitation duration and vital signs before recovery were collected in both groups,and the clinical data were analyzed. Results Age,gender and history of cardiovascular disease showed no significant difference between two groups( all P 0. 05). The mortality in the cardiovascular failure group was significantly higher than that in the non-cardiovascular failure group( P 0. 05). The APACHEⅡscore and SOFA score in the cardiovascular failure group were significantly higher than those in the non-cardiovascular failure group[( 30. 54 ± 8. 49) vs.( 19. 74 ± 9. 76),( 14. 65 ± 5. 14) vs.( 6. 49 ± 4. 56),P 0. 05]. The proportion of patients in initial onset of correctable cardiac dysrhythmia in the cardiovascular failure group was lower than that in the non-cardiovascular failure group( P 0. 05). Logistic regression analysis suggested that recovery duration,systemic inflammatory response syndrome( SIRS) before recovery,blood glucose disorders before recovery were independent influence factors for early cardiovascular failure. Conclusion Recovery duration,SIRS and blood glucose disorders before recovery are independent risk factors for early cardiovascular failure.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".