Nutritional Status During Inpatient Alcohol Detoxification
Bibliographic record
Abstract
AIMS: As low rates of thiamine are thought to be implicated in alcohol-related cognitive disorders, we wanted to assess patients with alcohol use disorders (AUD) during detoxification for their nutritional status and test if vitamins blood levels were associated with a surrogate of cognitive impairment. METHODS: We performed a retrospective chart review of medical records of 94 consecutive patients hospitalized for alcohol detoxification in a specialized addiction medicine department. Nutritional status was assessed with Body Mass Index (BMI). Vitamins blood levels were available for 80 patients, but thiamine only for 52 patients. The Montreal Cognitive Assessment (MoCA) score was used to screen for cognitive impairment at Day 10 of entry and was available in 59 patients. A binary logistic regression was performed to identify factors associated with MoCA scores below the threshold (26 points). RESULTS: The mean BMI was 23.28 ± 3.78 kg/m2 and 8.79% of weighted patients qualified for malnutrition. The mean MoCA score was 22.75 ± 4.88 points, and 66% of tested patients were below the threshold of suspected cognitive impairment. No low blood thiamine level was found. In multivariate analysis, BMI, but not vitamins blood rates, was significantly associated with a pathological MoCA screening test. CONCLUSION: Clinical examination is more sensitive than biomarkers to determine malnourished AUD patients who are at-risk for cognitive impairment. Malnourished patients with AUD should receive a full neuropsychological testing. SUMMARY: This retrospective chart review study screened for cognitive disorders during alcohol inpatient detoxification with the MoCA test. Body mass index, but not vitamins blood rates, was associated with a pathological MoCA. Clinical examination is more sensitive than biomarkers to determine malnourished AUD patients who are at-risk for cognitive impairment.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".