The Course of Cognitive Performance during Inpatient Treatment in Patients with Alcohol Use Disorder with No, Mild or Major Neurocognitive Disorders
Bibliographic record
Abstract
AIMS: In patients with a history of chronic alcohol abuse, neurocognitive disorders (NCD) are not uncommon. The current study aimed to explore the course of cognitive performance, as measured by the Montreal Cognitive Assessment (MoCA), and everyday cognitive functioning, as measured by the Patient Competency Rating Scale (PCRS), in a large group of patients with alcohol use disorder (AUD) admitted to the Center of Excellence for Korsakov and Alcohol-related Cognitive Impairments. METHODS: A multiple time-series design was used, in which the MoCA was administered at three time points of assessment, and the PCRS was completed by both the patient and a clinician at two time points, all during clinical treatment. RESULTS: A total of 524 patients were included, 71 of whom were diagnosed with AUD only, 284 with AUD and mild NCD (ARCI) and 169 with AUD, major NCD and fulfilling criteria for Korsakoff's syndrome (KS). CONCLUSIONS: Cognitive performance improved for all three groups during treatment, sustained abstinence and recovery from AUD. A low memory performance on the MoCA without improvement over time was predictive for KS, while improvement on this domain did not differentiate between AUD and ARCI. Changes in overall cognitive performance and orientation in patients with KS were positively related to changes in everyday cognitive functioning.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".