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 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 | 0.009 | 0.069 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".