Clinical Decision Trees for Predicting Conversion from Cognitive Impairment No Dementia (CIND) to Dementia in a Longitudinal Population-Based Study
Bibliographic record
Abstract
The lack of gold standard diagnostic criteria for cognitive impairment in the absence of dementia has resulted in variable nomenclature, case definitions, outcomes, risk factors, and prognostic utilities. Our objective was to elucidate the clinical correlates of conversion to dementia in a longitudinal population-based sample. Using data from the Canadian Study of Health and Aging, a machine learning algorithm was used to identify symptoms that best differentiated converting from nonconverting cognitively impaired not demented participants. Poor retrieval was the sole predictor of conversion to dementia over 5 years. This finding suggests that patients with impaired retrieval are at greater risk for progression to dementia at follow-up. Employing significant predictors as markers for ongoing monitoring and assessment, rather than as clinical markers of conversion, is recommended given the less than optimal specificity of the predictive algorithms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".