Six-item cognitive impairment test (6-CIT)’s accuracy as a cognitive screening tool: best cut-off levels in emergency department setting
Bibliographic record
Abstract
Background: Nowadays, elderly patients represent a significant number of accesses to the Emergency Department (ED). Working rhythms do not allow to perform complete cognitive analysis, which would, however, be useful for the health care. This study aims to define the optimal cut-off values of the six-item Cognitive Impairment Test (6-CIT) as a cognitive screening tool in ED. Methods: This study included 215 subjects, evaluated at the Emergency Department of the University Hospital of Monserrato, Cagliari, Italy, from July to December 2021. The accuracy of 6-CIT as a cognitive screening tool was assessed by comparison with Mini Mental State Examination (MMSE). Results: < 0.0001). The 8/9 6-CIT cut-off score presented 86.76% sensitivity (CI: 76.4-93.8) and 91.84% specificity (CI: 86.2-95.7), and Youden index for this score was 0.786. Conclusion: Our study demonstrates that 6-CIT is a reliable cognitive screening tool in ED, offering excellent sensitivity and specificity with a 8/9 points cut-off score.
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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 | 0.001 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".