Evaluating the real-world representativeness of participants with mild cognitive impairment in Canadian research protocols: a comparison of the characteristics of a memory clinic patients and research samples
Bibliographic record
Abstract
BACKGROUND: Studies of mild cognitive impairment (MCI) employ rigorous eligibility criteria, resulting in sampling that may not be representative of the broader clinical population. OBJECTIVE: To compare the characteristics of MCI patients in a Calgary memory clinic to those of MCI participants in published Canadian studies. METHODS: Clinic participants included 555 MCI patients from the PROspective Registry of Persons with Memory SyMPToms (PROMPT) registry in Calgary. Research participants included 4,981 individuals with MCI pooled from a systematic literature review of 112 original, English-language peer-reviewed Canadian studies. Both samples were compared on baseline sociodemographic variables, medical and psychiatric comorbidities, and cognitive performance for MCI due to Alzheimer's disease and Parkinson's disease. RESULTS: Overall, clinic patients tended to be younger, more often male, and more educated than research participants. Psychiatric disorders, traumatic brain injury, and sensory impairment were commonplace in PROMPT (up to 83% affected) but > 80% studies in the systematic review excluded these conditions. PROMPT patients also performed worse on global cognition measures than did research participants. CONCLUSION: Stringent eligibility criteria in Canadian research studies excluded a considerable subset of MCI patients with comorbid medical or psychiatric conditions. This exclusion may contribute to differences in cognitive performance and outcomes compared to real-world clinical samples.
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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 | 0.161 | 0.300 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".