VADEC program: Cognitive impairment (CI) assessment in Spain
Bibliographic record
Abstract
Abstract Background Analyze the controversies in diagnosis and neuropsychological approach of patients with CI. Achieve a consensus of Good Clinical Practice Guidelines (GCPG) that stablish which cognitive tests are suitable. Method VADEC program is based on Delphi Methodology in order to reach to a consensus about diagnostic criteria and cognitive assessment scales more suitable in CI, and its relationship with epidemiologic and diagnostic guidelines or bibliographic sources published. The specific questionnaire was completed by neurologists specialized in dementia in Spain. It was proposed many items or questions (1‐9 scores), and decide the median value, the confidence degree and the group consensus achieved. Result The program was developed from 1st June to 31th July 2017. 130 neurologists were involved (21 years of clinical practice) in public system (90.8%). About 38.67 % of patients had CI, 41.15% moderate and 20.18% severe. There was a consensus of using Petersen criteria to define MCI as a syndrome. MMSE is considered the most useful tool, followed by Clock Drawing test (CDT), Friedman or MOCA test. It´s necessary to have more information, over all in DSM‐V, and the convenience of elaborating a GCPG which harmonized MCI criteria. Conclusion There was a great consensus about using MMSE or MEC test. There was a wide consensus about the utility of MoCA, CDT and Friedman test. These results express the necessity of GCPG in CI about the use of cognitive test in Spain.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".