The impact of prolonged high-concentration cortisol exposure on cognitive function and risk factors: Evidence from Cushing's disease patients
Bibliographic record
Abstract
Background: Prolonged high-concentration cortisol exposure may impair cognitive function, but its mechanisms and risk factors remain unclear in humans. Objective: Using Cushing's disease patients as a model, this study explores these effects and develops a predictive model to aid in managing high-risk patients. Methods: This single-center retrospective study included 107 Cushing's disease patients (January 2020-January 2024) at the First Medical Center of the PLA General Hospital. Cognitive function, assessed using the Montreal Cognitive Assessment, revealed 58 patients with cognitive impairment and 49 with normal cognitive function. Patients were divided into training (n = 53) and validation cohorts (n = 54) for constructing and validating the predictive model. Risk factors were identified via univariate analysis and least absolute shrinkage and selection operator regression, and a nomogram prediction model was developed. Performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: Cortisol AM/PM ratio, 8 a.m. cortisol concentration, body mass index, and fasting plasma glucose were significant risk factors for cognitive impairment. The nomogram demonstrated strong predictive ability, with ROC values of 0.80 (training) and 0.91 (validation). DCA indicated superior clinical utility compared to treating all or no patients. Conclusions: This study confirms the significant impact of prolonged high cortisol exposure on cognitive function and identifies key risk factors. The nomogram model offers robust performance, providing a valuable tool for managing Cushing's disease patients' cognitive health and informing strategies for other cortisol-related disorders.
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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 | 0.002 | 0.008 |
| 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 | 0.000 |
| 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".