Association between alcohol consumption/alcohol use disorders and patient complexity: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: The objective was to clarify the relationship between alcohol consumption/alcohol use disorders and patient complexity. DESIGN: Cross-sectional study. SETTING: A clinic located on a remote island in Okinawa, Japan, providing general outpatient practices and round-the-clock emergency services. PARTICIPANTS: Patients who lived on the island, visited Tarama Clinic from 1 April 2018 to 30 June 2018, were aged ≥20 years and had decision-making capacity were judged to be eligible for this study. MAIN OUTCOME MEASURES: Alcohol consumption/alcohol use disorders as measured by the Alcohol Use Disorders Identification Test (AUDIT) and patient complexity as scored by the Patient Centered Assessment Method (PCAM). RESULTS: During the 3-month study period, 355 patients (163 women and 192 men) with mean (SD) age of 66.4 (13.6) years were included. Multiple regression analysis of PCAM scores showed that, after adjusting for age, sex, education, occupation, physical activity, smoking, annual medical expenses and number of family members living with the patient, AUDIT scores classified as 'dependence likely' were associated with PCAM scores (p value=0.040). CONCLUSIONS: Alcohol consumption and alcohol use disorders classified as dependence likely are associated with patient complexity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".