Identifying prognostic factors of dementia in individuals with mild cognitive impairment (MCI): Are statistical models adequate?
Bibliographic record
Abstract
Abstract Background Identifying prognostic factors of MCI to dementia conversion is crucial. Most risk scores are developed based on selected variables using data‐driven methods from available variables in patient data. However, there is mixed evidence about the reliability of predictors selected based on data alone. Structured expert elicitation (SEE) is one approach to elicit clinician expert knowledge about relevant predictors for dementia risk. This study aims to identify critical predictors of dementia risk in persons with MCI from both SEE and random survival forests (RSF) and compare the differences between the two approaches. Method Clinical expert knowledge was quantified using SEE methodology: 11 experts (6 neurologists, 3 geriatricians, and 2 geriatric psychiatrists) were recruited to complete two rounds of surveys to rank predictors of dementia risk. RSF variable importance was used to assess the rank order of the potential predictors, according to the corresponding reduction of predictive accuracy when the predictor is replaced with its random permutation value risk using data from the Prospective Registry for Persons with Memory Symptoms of the Cognitive Neurosciences Clinic at the University of Calgary (N = 273; 26 potential predictors). Result According to experts, the SEE revealed age and cerebrospinal fluid profile patterns consistent with Alzheimer’s disease (AD) as the two most important predictors of dementia risk in persons with MCI. In contrast, data‐driven RSF approach, using information from routine clinical practice without AD biomarkers, identified the Consortium to Establish a Registry for AD total score and the Montreal cognitive assessment (MoCA) total score as the most important predictors. Both approaches identified four common variables (among the top 10 predictors) included age, signs of parkinsonism, MoCA total score, and behaviour impairment. Conclusion Our findings revealed discrepancies between a data‐driven approach and SEE in identifying significant predictors of dementia in people with MCI. The SEE identified some crucial predictors, such as AD biomarker status, that are not routinely collected in registries and observational studies or available in widespread practice. Most published risk scores are not used in practice. This study offers a possible explanation: what is important to clinicians may be not included in the risk scores.
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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 | 0.075 | 0.216 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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".