The influencing factors of cognitive dysfunction in patients after cardiac surgery and the construction of a nomogram prediction model
Bibliographic record
Abstract
BACKGROUND: Early detection of cognitive dysfunction (POCD) in patients undergoing cardiac surgery may help improve the prognosis and quality of life. Identifying risk factors and clinically relevant factors is critical for prevention and treatment. METHODS: This study retrospectively selected 305 patients admitted to the cardiac surgery Department of Union Hospital Affiliated with Fujian Medical University from January 2024 to July 2024 as the study objects. The cognitive function of the patients was assessed by the Montreal Cognitive Assessment Scale (MOCA) before and on the 6th day after surgery, and the patients were divided into a cognitive dysfunction group and a non-cognitive dysfunction group. Logistic regression was used to analyze the risk factors of POCD in patients undergoing cardiac surgery. R software was used to construct the nomogram model of POCD in heart patients. RESULTS: = 8.73, P = 0.36 > 0.05) showed good consistency. The area under the ROC curve is 0.80, with a good differentiation and decision curve. CONCLUSIONS: Age, white blood cell count, lymphocyte, and hemoglobin are independent risk factors for POCD on day 6 of cardiac surgery. The nomogram prediction model constructed in this study has good predictive ability.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".