B-289 Assessing the Shift in Positivity Rates of Cocaine and Its Metabolites in a Community Based Population: A Transition from utilization of Cutoff Concentrations to Qualitative Structure Identification
Bibliographic record
Abstract
Abstract Background Semi-quantitative cutoff concentrations have traditionally been employed for urine drug detection. Research suggests that these cutoffs may be set too high, leading to the risk of overlooking positive cases. The advent of liquid chromatography-high-resolution mass spectrometry (LC-HR/MS) technology allows for the detection of drugs at lower concentrations. This study aims to assess the impact of transitioning from cutoff concentrations to qualitative structure identification on the positivity rates of cocaine and its metabolites in a community-based population. Methods Data analysis was conducted on results obtained from a community laboratory from April 2021 to October 2023. Screening of drugs was carried out using the Sciex X500R QTOF MS System. Cutoff concentrations were applied from April 2021 to September 2022 for screening cocaine and its metabolites (benzoylecgonine, norcocaine, and cocaethylene) in 62,816 samples. From October 2022 to October 2023, a qualitative structure identification approach was adopted for screening 47,676 samples. Results The previous screening for cocaine, benzoylecgonine, norcocaine, and cocaethylene was performed with a cutoff concentration of 50 ng/mL. Employing a qualitative structure identification approach, cocaine, benzoylecgonine, and cocaethylene were detectable at 5 ng/mL, while norcocaine could be identified at 10 ng/mL. Consequently, when qualitative identification was applied, the positivity rates for cocaine and its metabolites showed an increase. The prevalence of cocaine and its metabolites in the positive samples also demonstrated a corresponding shift. Conclusions Moving from using cutoff concentrations to employing qualitative structure identification with LC-HR/MS markedly enhances the detection of positive cases for cocaine and its metabolites. Although the detection sensitivity of drugs is improved, the presence of drugs at low concentrations can pose challenges with interpretation of results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".