The development of "clinically sensible" tools to screen for cognitive impairment in community-dwelling elderly persons. Bridging the gap between research and clinical practice by balancing discriminant ability vs. practicality.
Bibliographic record
Abstract
Background. Despite its prevalence and clinical relevance, cognitive impairment typically remains undetected in 50% of cases. Objective. To develop clinically sensible (quick, simple, acceptable), accurate and readily recalled screens for cognitive impairment based on easily reproducible analytic strategies. Methods. The Canadian Study of Health and Aging (CSHA-1) served as the derivation data set. 3MS cognitive screening questions which were judged as most likely to be employed by busy clinicians and which were significantly associated (via chi2 analysis) with cognitive impairment were selected as independent variables for multivariate analysis. The screening tests derived from logistic regression and recursive partitioning analyses which most closely approximated the sensitivity and specificity of the entire 3MS were externally validated. Results. Two logistic regression based scales and two recursive partitioning algorithms demonstrated sensitivities and specificities approaching those of the complete 3MS (approximately 80% and 60% respectively). The sensitivity was superior to that of the MMSE. Conclusion. Readily reproducible multivariate analysis based strategies can be developed which generate practical screening tests with psychometric properties approaching those of the 3MS. Given the existence of verification bias, these screens as well as screens with higher sensitivity and lower specificity must be validated prospectively before they can be clinically employed.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".