Detección y prevalencia del trastorno por uso de alcohol en los centros de atención primaria de Cataluña
Bibliographic record
Abstract
AIM: To describe the detection by general practitioners (GP) of alcohol use disorders (AUD) and alcohol dependence, and their prevalence in primary health settings. DESIGN: Cross-sectional study. SETTINGS: Twenty Catalan primary health care centres (Spain). PARTICIPANTS AND MEASUREMENTS: Twenty three randomly selected GP were surveyed about alcohol and other diseases of their patients. A total of 1,372 patient interviews were collected. Patients and GPs were asked about AUD and other mental and health conditions. The Composite International Diagnostic Interview (CIDI) as the gold standard was used, as well as other structured interviews (K10 screening and World Health Organization Disability Assessment Schedule 2.0). RESULTS: The CIDI diagnosed 9.6% of the total sample with an AUD, and 4.8% diagnosed by GPs. CIDI could detect more AUD in young adults, while GPs diagnosed more AUD and alcohol dependence in elderly people, who also had more health conditions. GPs recognised AUD in 28.8% of patients diagnosed with CIDI, but 42.4% of patients diagnosed by GPs were not detected with CIDI. Taking both into consideration, the gold standard and the GP clinical impression, 11.7% of patients had an AUD and 8.6% an AD. CONCLUSIONS: GP recognise AUD better in the elderly with worst health conditions than CIDI. AUD and alcohol dependence prevalence is high in primary health care centres.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".