Surveillance of drug abuse in Hong Kong by hair analysis using LC‐MS/MS
Bibliographic record
Abstract
The aim of this study is to reveal the habits of drug abusers in hair samples from drug rehabilitation units in Hong Kong. With the application of liquid chromatography-tandem mass spectrometry (LC-MS/MS) technology, a total of 1771 hair samples were analyzed during the period of hair testing service (January 2012 to March 2016) provided to 14 drug rehabilitation units including non-governmental organizations (NGOs), rehabilitation centers, and medical clinics. Hair samples were analyzed for abused drugs and their metabolites simultaneously, including ketamine, norketamine, cocaine, benzoylecgonine, cocaethylene, norcocaine, codeine, MDMA, MDA, MDEA, amphetamine, methamphetamine, morphine, 6-acetylmorphine, phencyclidine, and methadone. The results showed that ketamine (77.2%), cocaine (21.3%), and methamphetamine (16.5%) were the frequently detected drugs among those drug abusers, which is consistent with the reported data. In addition, the usage of multiple drugs was also observed in the hair samples. About 29% of drug-positive samples were detected with multiple drug use. Our studies prove that our locally developed hair drug-testing method and service can be a valid tool to monitor the use of abused drugs, and which could facilitate rehabilitation program management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".