Does Low Urine Creatinine Level Indicate the Presence of Urine Alcohol in Methadone Maintenance Treatment Patients?
Bibliographic record
Abstract
OBJECTIVE: We sought to test the assumption that a low urine creatinine level is indicative of the presence of alcohol in the urine of patients prescribed methadone. METHODS: This is a medical record review of 261,055 urine samples from approximately 6,000 patients prescribed methadone during a one-year period and for whom both urine creatinine and ethanol levels were simultaneously measured. We defined a creatinine level of less than 2.26 mmol/L as 'low' used a urine ethanol level of greater than 2.0 mmol/L as the reference standard for alcohol consumption. RESULTS: The sensitivity and specificity of low urine creatinine as a marker for the detection of urine ethanol are 11.9% (95% CI: 11.3, 12.5%) and 96.7% (95% CI: 96.7, 96.7%), respectively. In this patient population with a low (3.6%) prevalence of alcohol in the urine, the results correspond to a positive predictive value of 11.9% (95% CI: 11.3, 12.6%) and a negative predictive value of 96.7% (95% CI: 96.7, 96.7%), respectively. CONCLUSIONS: Low urine creatinine is a poor screening test for detecting alcohol consumption among patients on methadone. However, a normal creatinine level has a 96.7% probability of no alcohol urine present in the urine.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".