Detection of 30 Fentanyl Analogs by Commercial Immunoassay Kits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Health-care workers, laboratorians and overdose prevention centers rely on commercial immunoassays to detect the presence of fentanyl; however, the cross-reactivity of fentanyl analogs with these kits is largely unknown. To address this, we conducted a pilot study evaluating the detection of 30 fentanyl analogs and metabolites by 19 commercially available kits (9 lateral flow assays, 7 heterogeneous immunoassays and 3 homogenous immunoassays). The analogs selected for analysis were compiled from the Drug Enforcement Administration and National Forensic Laboratory Information System reports from 2015 to 2018. In general, the immunoassays tested were able to detect their intended fentanyl analog and some closely related analogs, but more structurally diverse analogs, including 4-methoxy-butyryl fentanyl and 3-methylfentanyl, were not well detected. Carfentanil was only detected by kits specifically designed for its recognition. In general, analogs with group additions to the piperidine, or bulky rings or long alkyl chain modifications in the N-aryl or alkyl amide regions, were poorly detected compared to other types of modifications. This preliminary information is useful for screening diagnostic, forensic and unknown powder samples for the presence of fentanyl analogs and guiding future testing improvements.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it