El Montreal Cognitive Assessment (moca) comoscreening cognitivo en pacientes con trastorno por consumo de alcohol: Un estudio Delphi
Bibliographic record
Abstract
espanolEl deterioro cognitivo en personas con tras-torno por uso de alcohol puede pasar desaper-cibido si no se utilizan medidas objetivas paraevaluarlo. Se realizo un estudio Delphi modi-ficado para conocer la opinion de un panel de40 expertos sobre la adecuacion del MontrealCognitive Assessment. La mayoria de los pro-fesionales consultados utilizan el MinimentalState Examination y el Test del Reloj, instru-mentos que se han mostrado poco sensibles aldeterioro cognitivo en esta poblacion. La ma-yoria de los consultados consideran el MoCAadecuado y suficientemente exhaustivo, siem-pre y cuando no sustituya a una exploracionneuropsicologica completa posterior EnglishCognitive impairment might go undetec-ted in people with alcohol use disorder ifnot measured with objective instruments.We conducted a modified Delphi study togather the opinions of a panel of 40 expertsfrom various disciplines about the suitabi-lity of the Montreal Cognitive Assessment(MoCA). The screening instrument mostwidely used by respondents was the Mini-Mental State Examination (MMSE), despitethe fact that it shows low sensitivity in thispopulation. Most of the respondents con-sidered the MoCA to be suitable and suffi-ciently exhaustive, provided it does notreplace a subsequent and full neuropsycho-logical examination.
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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 | 0.035 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".