Clinician Suspicion of an Alcohol Problem: An Observational Study From the AAFP National Research Network
Bibliographic record
Abstract
PURPOSE: In clinical practice, detection of alcohol problems often relies on clinician suspicion instead of using a screening instrument. We assessed the sensitivity, specificity, and predictive values of clinician suspicion compared with screening-detected alcohol problems in patients. METHODS: We undertook a cross-sectional study of 94 primary care clinicians' office visits. Brief questionnaires were completed separately after a visit by both clinicians and eligible patients. The patient's anonymous exit questionnaire screened for hazardous drinking based on the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) and for harmful drinking (alcohol abuse or dependence) based on 2 questions from the Diagnostic and Statistical Manual of Mental Disorders. After the visit, clinicians responded to the question, "Does this patient have problems with alcohol?" with answer options including "yes, hazardous drinking" and "yes, alcohol abuse or dependence." Analyses assessed the associations between patients' responses to screening questions and clinician's suspicions. RESULTS: Of 2,518 patients with an office visit, 2,173 were eligible, and 1,699 (78%) completed the exit questionnaire. One hundred seventy-one (10.1%) patients had a positive screening test for hazardous drinking (an AUDIT-C score of 5 or greater) and 64 (3.8%) for harmful drinking. Clinicians suspected alcohol problems in 81 patients (hazardous drinking in 37, harmful drinking in 40, and both in 4). The sensitivity of clinician suspicion of either hazardous or harmful drinking was 27% and the specificity was 98%. Positive and negative predictive values were 62% and 92%, respectively. CONCLUSION: Clinician suspicion of alcohol problems had poor sensitivity but high specificity for identifying patients who had a positive screening test for alcohol problems. These data support the routine use of a screening tool to supplement clinicians' suspicions, which already provide reasonable positive predictive value.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".