Index Variables for Studying Outcomes in Vascular Cognitive Impairment
Bibliographic record
Abstract
Multivariable modeling in dementia risk factor studies is limited by the number of factors that can be analyzed practicably. Index variables, which integrate exposures, can efficiently reduce dimensionality. The Consortium to Investigate Vascular Impairment of Cognition study, a Canadian memory-clinic-based 30-month cohort study of 1,347 patients, used a vascular risk factor index (from 20 exposures) and a vascular clinical profile index (17 items). Patients with vascular cognitive impairment had higher index counts compared to those without cognitive impairment (0.16 +/- 0.11 vs. 0.07 +/- 0.07 for the risk factor index and 0.21 +/- 0.16 vs. 0.09 +/- 0.07 for the clinical profile index; p < 0.05). Both the death rate and the rate of cognitive impairment increased exponentially with the index variable (r > 0.90 for each index). The risk ratio for death was 1.12 (95% CI 1.09-1.15) for each increment of the risk factor index and was 1.23 (95% CI 1.1-1.28) for each increment of the clinical profile index. With each index, the areas under the receiver operating characteristic curves for predicting death and institutionalization ranged from 0.73 +/- 0.01 to 0.75 +/- 0.01. Construction of index variables that integrate multidimensional factors is a promising approach to assessing risk in multi-determined states.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".