Sensitivity and specificity of 5 min cognitive screening tests in patients with acute coronary syndrome
Bibliographic record
Abstract
AIMS: This study aimed to determine the sensitivity and specificity of the National Institute of Neurological Disorders and Stroke (NINDS) and the Canadian Stroke Network (CSN) brief (5 min) screen composed of three items of the Montreal Cognitive Assessment (MoCA), in acute coronary syndrome (ACS) patients during hospital admission, relative to the full MoCA and potential alternative combinations of other items. METHODS AND RESULTS: Participants were consecutively recruited during ACS admission and administered the MoCA before discharge. The three NINDS-CSN screen items were extracted, collated and compared to the full MoCA. Receiver operator characteristic (ROC) curves were created to determine the sensitivity, specificity, and appropriate cut-off scores of the screens. The mean age of the sample (n = 81) was 63.49 [standard deviation (SD) 10.85] years and 49.4% screened positive for cognitive impairment. The NINDS-CSN mean score was 9.22 (SD 2.09 of the potential range 0-12). Area under the ROC (AUC) indicated high accuracy levels for screening for cognitive impairment (AUC = 0.89, P < 0.01, 95% confidence interval 0.82, 0.96) with none of the alternative combination screens performing better on both sensitivity and specificity. A cut-off score of ≤10 on the NINDS-CSN protocol provided 83% sensitivity and 80% specificity for classifying cognitive impairment. CONCLUSION: The NINDS-CSN protocol presents an accurate, feasible screen for cognitive impairment in patients following ACS for use at the bedside and potentially also for telephone screens. Diagnostic accuracy should be confirmed using a neurocognitive battery.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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".