Identification of Major Cognitive Disorders in Self-Reported versus Administrative Health Data: A Cohort Study in Quebec
Bibliographic record
Abstract
BACKGROUND: The first imperative in producing the relevant and needed knowledge about major neurocognitive disorder (MNCD) is to identify people presenting with the condition adequately. To document potential disparities between administrative health databases and population-based surveys could help identify specific challenges in this population and methodological shortfalls. OBJECTIVE: To describe and compare the characteristics of community-dwelling older adults according to four groups: 1) No MNCD; 2) Self-reported MNCD only; 3) MNCD in administrative health data only; 4) MNCD in both self-reported and administrative health data. METHODS: This retrospective cohort study used the Care Trajectories-Enriched Data (TorSaDE) cohort, a linkage between five waves of the Canadian Community Health Survey (CCHS) and health administrative health data. We included older adults living in the community who participated in at least one cycle of the CCHS. We reported on positive and negative MNCD in self-reported versus administrative health data. We then compared groups' characteristics using chi-square tests and ANOVA. RESULTS: The study cohort was composed of 25,125 older adults, of which 784 (3.1%) had MNCD. About 70% of people with an MNCD identified in administrative health data did not report it in the CCHS. The four groups present specific challenges related to the importance of perception, timely diagnosis, and the caregivers' roles in reporting health information. CONCLUSION: To a certain degree, both data sources fail to consider subgroups experiencing issues related to MNCD; studies like ours provide insight to understand their characteristics and needs better.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".