Informant‐based tools for assessment and monitoring of cognition, behavior, and function in neurocognitive disorders: Systematic review and report from a CCCDTD5 Working Group
Bibliographic record
Abstract
OBJECTIVE: As part of the fifth Canadian Consensus Conference on the Diagnosis and Treatment of Dementia, we assessed the literature on informant-based tools for assessment and monitoring of cognition, behavior, and function in neurocognitive disorders (NCDs) to provide evidence-based recommendations for clinicians and researchers. METHODS: A systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses standards guidelines. Publications that validated the informant-based tools or described their key properties were reviewed. Quality of the studies was assessed using the modified Quality Assessment tool for Diagnostic Accuracy Studies. RESULTS: Out of 386 publications identified through systematic search, 34 that described 19 informant-based tools were included in the final review. Most of these tools are backed by good-quality studies and are appropriate to use in clinical care or research. The tools vary in their psychometric properties, domains covered, comprehensiveness, completion time, and ability to detect longitudinal change. Based on these properties, we identify different tools that may be appropriate for primary care, specialized memory clinic, or research settings. We also identify barriers to use of these tools in routine clinical practice. CONCLUSION: There are several good-quality tools available to collect informant-report for assessment and monitoring of cognition, behavior, or function in patients with NCDs. Clinicians and researchers may choose a particular tool based on their specific needs such as domains of interest, desired psychometric properties, and feasibility. Further work is needed to make the tools more user-friendly and to adopt them into routine clinical care.
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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 | 0.078 | 0.192 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.025 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".