Bibliographic record
Abstract
The diagnostic accuracy of the short Montreal Cognitive Assessment (s-MoCA), a cognitive screening instrument recently derived by item response theory and computerized adaptive testing from the original MoCA, for the diagnosis of dementia and mild cognitive impairment (MCI) was assessed in 2 patient cohorts referred to a dedicated memory clinic in order to examine the validity and reproducibility of s-MoCA. Diagnosis used standard clinical diagnostic criteria for dementia and MCI as reference standard (prevalence of cognitive impairment = 0.43 and 0.46 in each cohort, respectively). There were significant differences in s-MoCA test scores for dementia, MCI, and subjective memory impairment ( P ≤ .01), and s-MoCA effect sizes (Cohen d) were medium to large (range: 0.65-1.42) for the diagnosis of dementia and MCI. Using the cut-off for s-MoCA specified in the index study, it proved highly sensitive (>0.9) for diagnosis of dementia but with poor specificity (≤0.25), with moderate sensitivity (≥0.75) and specificity (≥0.60) for diagnosis of MCI. In conclusion, in these pragmatic diagnostic test accuracy studies, s-MoCA proved acceptable and sensitive for the diagnosis of cognitive impairment in a memory clinic setting, with a performance similar to that of the original MoCA.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".