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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".