Bibliographic record
Abstract
BACKGROUND: Vascular causes and factors remain the most significant preventable component of cognitive disorders of elderly individuals. The Hachinski Ischemic Score (HIS) is the questionnaire most commonly used for diagnosis of vascular dementia. OBJECTIVE: To consolidate and further validate the HIS. DESIGN: The Canadian Study for Health and Aging was used for this study. It was a cohort study conducted in 3 waves in 1991, 1996-1997, and 2001-2002. The HIS containing 13 items was subjected to correspondence analysis to identify its optimal scaling of item scores and minimal set of items while maximizing the explainable variance. SETTING: A community-based cohort study. PATIENTS: For this analysis, we used 2968 of 3054 well-characterized and well-diagnosed cases with complete HIS data (86 cases had ≥1 item missing) from Canadian Study for Health and Aging phases 2 (1996-1997; n = 2431) and 3 (2001-2002; n = 623). RESULTS: Two optimized HIS versions were identified that classify patients with vascular dementia vs those with nonvascular dementia as well as or more accurately than the original HIS instrument. Assuming the HIS instrument measures only a single dimension, correspondence analysis identified the 7 most discriminative HIS items. Binary scoring (0, 1) of these items led to a 7-item HIS model that classified as well as the original 13-item HIS instrument. By merging highly similar HIS items and applying correspondence analysis, a 5-item composite HIS model was created that measures 2 meaningful dimensions of information and classified vascular vs nonvascular dementia better than the original HIS instrument. Each HIS version developed has specific advantages and disadvantages in terms of simplicity, scoring, generalizability, and accuracy. CONCLUSION: Depending on the specific setting, 2 reduced HIS versions consisting of 5 composite-question items or 7 single-question items classify as well as or better than the original HIS instrument.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".