Postoperative cognitive dysfunction in heart transplantation recipients
Bibliographic record
Abstract
OBJECTIVE: This study aimed to investigate the occurrence and risk factors of postoperative neurocognitive disorder (NCD) in patients who underwent heart transplantation. METHODS: Seventy-six heart transplant patients were analyzed for clinical data including gender, age, height, weight, education level, left ventricular ejection fraction (LVEF), stroke volume (SV), transplantation duration, and pretransplant medical history. Cognitive function was assessed using the mini-mental status examination (MMSE) and Montreal cognitive assessment (MoCA) scales. Patients were categorized into cognitively normal and impaired groups based on the presence or absence of cognitive dysfunction, and their cognitive function scores were compared. Multivariate logistic regression was used to identify independent risk factors for cognitive impairment in postoperative cardiac transplant patients. RESULTS: Cognitive dysfunction was observed in 48 out of 76 heart transplant patients, representing an incidence of 63.2%. Cognitive impairment in heart transplant recipients predominantly affected multiple cognitive domains. Logistic regression analysis identified age (OR = 1.057, 95% CI 1.002-1.115), gender (OR = .200, 95% CI .044-.919), education level (OR = .728, 95% CI .600-.883), LVEF (OR = .891, 95% CI .820-.969), and history of diabetes (OR = 7.674, 95% CI 1.317-44.733) as independent risk factors for postoperative NCD in heart transplant recipients (P < .05). CONCLUSION: The study found a high incidence of postoperative NCD in heart transplant patients, with gender, age, education level, LVEF, and diabetes history being significant risk factors. Early identification and intervention targeting these risk factors may help prevent NCD in postheart transplant patients and improve long-term outcomes.
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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 | 0.001 |
| 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 | 0.000 |
| 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".