Validation of the Clinical Dementia Rating scale without informant
Bibliographic record
Abstract
BACKGROUND: There are over 55 million individuals living with dementia worldwide, reaching 10 million new cases every year according to the World Health Organization. A crucial first step is identifying individuals at risk of dementia and trying to prevent of delay the progression, since up to 40% of dementia cases are potentially preventable. Thus, tools such as the Clinical Dementia Rating scale (CDR), the gold-standard in staging dementia, are crucial. However, the CDR requires the presence of an informant (e.g. spouse, caregiver) which is a challenge for 30% of Canadians who live alone or do not have an informant available or willing to answer questions. Hence, we have created a novel CDR version without the need of an informant, which will simplify the staging of dementia thus helping more individuals. Our study aims to validate the CDR without informant and we hypothesize a high level of agreement across the two versions. METHOD: The CDR without informant has been created by replacing the information obtained from the informant with goal-oriented questions asked directly to the participant. Cognitively impaired participants (score below 26 on the Montreal Cognitive Assessment (MoCA)) (n = 60) already recruited from the Gait and Brain study cohort will be administered both CDR versions during their scheduled annual assessment. Intraclass correlation and interrater reliability will be calculated (correlation of at least 0.6, assuming statistical power of 0.8 and alpha of 0.05). RESULT: The results of this study are in progress. CONCLUSION: Direct assessment of an individual allows for increased opportunity to classify progression from cognitive impairment to dementia, elucidating the importance of this new tool. This novel CDR without informant can help clinicians and researchers to assess those impacted with dementia, when caregiver information is not feasible to obtain or is unreliable.
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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 | 0.042 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".