Use of Hair Analysis for Confirmation of Self-Reported Cocaine Use in Users with Negative Urine Tests
Bibliographic record
Abstract
INTRODUCTION: Identification of cocaine use based on a urine test may miss many cases because of the short elimination half-life of the drug. Our objective was to verify the sensitivity of the cocaine hair test in admitted users. PATIENTS AND METHODS: Admitted cocaine users (38), that were 18-70 years of age and reported to have refrained from using cocaine in the few days to months prior to the test, were compared to 10 controls who claimed never to have used cocaine. All had negative urine tests for cocaine and benzoylecgonine by thin-layer chromatography. Cocaine and benzoylecgonine were extracted from unwashed hair and tested by established immunoassays. RESULTS: The hair test was positive in 37/38 cases (97%) and in none of the controls. There was significantly more cocaine in black hair than in brown or blonde hair per mg of cocaine dose reported to have been consumed, highlighting a potential bias when interpreting test results in individuals with dark hair. There was a statistically significant correlation between reported dose used and hair concentrations of cocaine. DISCUSSION: The cocaine hair test appears to be highly sensitive and specific in identifying past cocaine use in the setting of a negative urine test.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".