Feasibility of a standard cognitive assessment in European academic memory clinics
Bibliographic record
Abstract
INTRODUCTION: Standardized cognitive assessment would enhance diagnostic reliability across memory clinics. An expert consensus adapted the Uniform Dataset (UDS)-3 for European centers, the clinician's UDS (cUDS). This study assessed its implementation acceptability and feasibility. METHODS: We developed a survey investigating barriers, facilitators, and willingness to implement the cUDS. With a mixed-methods design, we analyzed data from academic memory clinics. RESULTS: Seventy-eight percent of responding clinicians were experienced neuropsychologists/psychologists and 22% were medical specialists coming from 18 European countries. Sixty-five percent clinicians were willing to implement cUDS. General barriers related to implementation (43%) and clinical-methodological domains (21%). Favorable clinicians reported finances (15%) and digitalization (9%) as facilitating, but unavailability of local norms (23%) as hindering. Unfavorable clinicians reported logistical (23%) and time issues (18%). DISCUSSION: Despite challenges, data showed moderate clinicians' acceptability and requirements to improve feasibility. Nonetheless, these results come from academic clinicians. The next steps will require feasibility evaluation in non-academic contexts.
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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 | 0.091 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".