The CAGE Questionnaire for Alcohol Misuse: A Review of Reliability and Validity Studies
Bibliographic record
Abstract
PURPOSE: To review the reliability and validity of the CAGE questionnaire across different patient populations and discuss its role in the detection of alcohol-related problems. METHODS: The Cochrane Database for Systematic Reviews, Medline, Embase, and Psychinfo were searched. No systematic reviews were found on the Cochrane Database. Search of the other databases yielded one systematic review and one meta-analysis, on different aspects of CAGE. Three articles on reliability and 16 on validity of CAGE were found and used. Studies generally yielded Level II evidence. RESULTS: CAGE has demonstrated high test-retest reliability (0.80-0.95), and adequate correlations (0.48-0.70) with other screening instruments. The questionnaire is a valid tool for detecting alcohol abuse and dependence in medical and surgical inpatients, ambulatory medical patients, and psychiatric inpatients (average sensitivity 0.71, specificity 0.90). Its performance in primary care patients has been varied, while it has not performed well in white women, prenatal women, and college students. Furthermore, it is not an appropriate screening test for less severe forms of drinking. CONCLUSIONS: CAGE is short, feasible to use, and easily applied in clinical practice. However, users should be aware of its limitations when interpreting the results. A positive screen should be followed by a proper diagnostic evaluation using standard clinical criteria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".