Comparison of the Six Item Cognitive Impairment Test (6CIT) to Commonly-Used Short Cognitive Screening Instruments in a Memory Clinic Population
Bibliographic record
Abstract
Background: Short cognitive screening instruments (CSI) are required to identify cognitive impairment in busy outpatient clinics. While the Six Item Cognitive Impairment Test (6CIT) is commonly used, its accuracy in those with mild cognitive impairment (MCI) and subjective cognitive decline (SCD) and against more widely-used CSIs is less well established. Objective: To examine the diagnostic accuracy of the 6CIT against the Montreal Cognitive Assessment (MoCA) and Quick Mild Cognitive Impairment (Q mci) screen across the cognitive spectrum in a memory clinic population. Methods: In total, 142 paired assessments were available (21 with SCD, 32 MCI, and 89 with dementia). Consecutive patients underwent a comprehensive assessment and were screened using the 6CIT, Q mci, and MoCA. Accuracy was determined from the area under receiver operating characteristic curves (AUC). Results: The median age of patients was 76 (±11) years; 68% were female. The median 6CIT score was 10/28 (±14). The 6CIT was strongly, negatively, and statistically significantly correlated with the Q mci ( r = –0.84) and MoCA ( r = –0.86). The 6CIT had good accuracy for separating cognitive impairment (MCI or dementia) from SCD, (AUC:0.88; 0.82–0.94), similar to the MoCA (AUC:0.92; 0.87–0.97, p = 0.308), but statistically lower than the Q mci (AUC:0.96; 0.94–0.99, p = 0.01). The 6CIT was faster to administer, median time 2.05 minutes versus 4.38 and 9.5 for the Q mci and MoCA, respectively. Conclusion: While the Q mci was more accurate than the 6CIT, the shorter administration time of the 6CIT, suggests it may be useful when assessing or monitoring cognitive impairment in busy memory clinics, though larger samples are required to evaluate.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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".