One Size Does Not Fit All: Choosing Practical Cognitive Screening Tools for Your Practice
Bibliographic record
Abstract
Every year, millions of patients worldwide undergo cognitive testing. Unfortunately, new barriers to the use of free open access cognitive screening tools have arisen over time, making accessibility of tools unstable. This article is in follow-up to an editorial discussing alternative cognitive screening tools for those who cannot afford the costs of the Mini-Mental State Examination and Montreal Cognitive Assessment (see www.dementiascreen.ca). The current article outlines an emerging disruptive "free-to-fee" cycle where free open access cognitive screening tools are integrated into clinical practice and guidelines, where fees are then levied for the use of the tools, resulting in clinicians moving on to other tools. This article provides recommendations on means to break this cycle, including the development of tool kits of valid cognitive screening tools that authors have contracted not to charge for (i.e., have agreed to keep free open access). The PRACTICAL.1 Criteria (PRACTIcing Clinician Accessibility and Logistical Criteria Version 1) are introduced to help clinicians select from validated cognitive screening tools, considering barriers and facilitators, such as whether the cognitive screening tools are easy to score and free of cost. It is suggested that future systematic reviews embed the PRACTICAL.1 criteria, or refined future versions, as part of the standard of review. Methodological issues, the need for open access training to insure proper use of cognitive screening tools, and the need to anticipate growing ethnolinguistic diversity by developing tools that are less sensitive to educational, cultural, and linguistic bias are discussed in this opinion piece. J Am Geriatr Soc 68:2207-2213, 2020.
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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 | 0.150 | 0.506 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.013 | 0.029 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".