Validity of a novel screen for cognitive impairment and neuropsychiatric symptoms in cardiac rehabilitation
Bibliographic record
Abstract
BACKGROUND: Currently, there is no composite screening tool that can efficiently and effectively assess prevalent yet under-recognized cognitive and neuropsychiatric comorbidities in patients with cardiovascular disease. We aimed to determine the validity and feasibility of a novel screen assessing cognitive impairment, anxiety, apathy and depression (CAAD screen) in those attending cardiac rehabilitation (CR). METHODS: All patients diagnosed with cardiovascular disease or cardiovascular risk factors entering CR were screened as part of clinical care. A subset of those patients agreed to complete validation assessments (n = 127). Screen results were compared to widely accepted standards for cognition, anxiety, apathy, and depression using a modified receiver operating characteristic (ROC) and area under the curve analysis. RESULTS: The screen was completed by 97% of participants in 10 min or less with an average completion time of approximately 5 min. Screening scores adjusted for age, sex and years of education had acceptable or excellent validity compared to widely accepted standard diagnoses: CAAD-Cog (AUC = 0.80); CAAD-Anx (AUC = 0.81); CAAD-Apathy (AUC = 0.79) and CAAD-Dep (AUC = 0.85). CONCLUSIONS: The CAAD screen may be a valid and feasible tool for detecting cognitive impairment, anxiety, apathy and depression in CR settings.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".