Recent trends in the analysis of GHB in hair
Bibliographic record
Abstract
Hair analysis is a valuable tool in forensic toxicology, providing extended detection windows and critical insights into drug testing, usage trends, and drug-facilitated crimes. This systematic review was conducted using Scopus, Web of Science, and PubMed databases from March 2017 to September 2024, and evaluated 19 studies (16 research articles and 3 case reports) on the detection of γ-hydroxybutyrate (GHB) in hair. This review examines recent studies on GHB concentrations in hair, focusing on both endogenous and exogenous concentrations resulting from illicit and prescribed use, as well as the analytical methods employed. This review includes decontamination parameters, extraction techniques, and sample sizes used during the analytical method. New studies report that endogenous GHB levels range from 0.2 to 5.5 ng/mg, while exogenous levels vary widely from 0.3 to 239.6 ng/mg. Additionally, published results indicate that the frequency of use may be more significant than the dosage for exogenous GHB to be incorporated into the hair. A novel adjacent segmentation method has been proposed to differentiate endogenous from exogenous GHB, identifying local peaks within adjacent hair segments. Research into GHB-glucuronide as a biomarker has found it unreliable due to inconsistent correlations with exogenous use. Further research is needed to refine the interpretation of GHB levels in forensic applications.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".