Characteristics of currently available informant‐based tools to obtain collateral information for diagnosis and monitoring of cognitive disorders
Bibliographic record
Abstract
Abstract Background To systematically review literature on informant based tools for the diagnosis and monitoring of neurocognitive disorders and make evidence based recommendations to clinicians and researchers as part of the 5th Canadian Consensus Conferences on the Diagnosis and Treatment of Dementia. Method A systematic search was performed in MEDLINE, PsycINFO, Cochrane, and EMBASE databases. Limits used for searches were humans, English, and age above 45. Articles that validated the tools and or described their key properties were included. The original search yielded 358 articles. After removing duplicates and reviewing titles and abstracts, 167 remained and were reviewed in full text. Eventually 92 articles that described 16 informant based tools were included for final review. Result Based on comprehensiveness and validity, 8 tools were found to be preferable for clinical and research use. AD8; Dementia Severity Rating Scale (DSRS); Everyday Cognition Scale (ECog); and the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) were selected for cognition and function and the Neuropsychiatric Inventory (NPI); Neuropsychiatric Inventory Questionnaire (NPI‐Q); Neuropsychiatric Inventory Clinician Rating scale (NPI‐C); and the Mild Behavior Impairment Checklist (MBI‐C) were selected for behaviour. Clinicians and researchers should choose a specific tool based on the need for comprehensive assessment versus desired efficiency depending on the setting. Conclusion There are a number of evidence based tools that are appropriate to use in clinical care and research settings to assess cognition, behavior and function. Tools can be chosen based on specific needs and available resources.
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 | 0.078 | 0.287 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.028 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".