REAL-WORLD REPRESENTATIVENESS OF CANADIAN RESEARCH SUBJECTS WITH MILD COGNITIVE IMPAIRMENT
Bibliographic record
Abstract
Abstract Studies of mild cognitive impairment (MCI) utilize stringent inclusion/exclusion criteria which may impact the generalizability of findings to the broader clinical population. We compared characteristics of MCI patients in a Canadian memory clinic in Calgary to MCI research participants in published Canadian studies to assess the representativeness of research samples. Clinic participants included 555 MCI patients from the Prospective Study for Persons with Memory Symptoms registry. Research participants included 4,981 individuals with MCI retained from a systematic literature review of 112 peer-reviewed empirical Canadian studies. Clinic patients and research participants were diagnosed with MCI using similar diagnostic criteria (i.e., from the NIA-AA, or Petersen criteria). Both samples were compared on baseline demographic variables, medical and psychiatric comorbidities, and global cognitive performance using chi-square tests and t-tests with weighted means. Diverse presumed causes were noted among clinic patients. Clinic patients were younger, more likely to be male, and more educated than research participants (ds: 0.22-0.98). Psychiatric disorders, traumatic brain injury, and sensory impairments were common in clinic patients (up to 83%), but participants with these conditions were excluded from approximately 80% of studies in the systematic review. Clinic patients performed significantly worse on two global cognitive assessments (ds: 0.53 – 1.27). Stringent eligibility criteria such as used in Canadian MCI research studies would exclude a considerable subset of MCI patients seen in our referral clinic. This may have contributed to the disparities between the clinical and research cohorts in the cognitive measures examined. The implications of these findings will be discussed.
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 | 0.090 | 0.232 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".