Evaluation and applicability of Alere iCup DX 14 for rapid postmortem urine drug screening at autopsy
Bibliographic record
Abstract
Performing point-of-care urine drug screen testing at autopsy by a forensic pathologist may provide an early indication of the presence of analytes of interest during autopsy. An evaluation for the screening of 14 classes of common drugs of abuse in postmortem urine by the point-of-care screening device, Alere iCup DX 14, is presented. One hundred ninety postmortem urine samples were screened with the iCup occurring at autopsy by the forensic pathologist. Positive and negative results obtained from the screening kit were evaluated against confirmatory test results obtained using routine forensic toxicology analyses that employed LC-MS/MS and GC-MS to detect a combination of over 85 common drugs of abuse and medications. Sensitivity for each respective iCup drug class ranged from 66% (buprenorphine) to 100% (methadone, tricyclic antidepressants). Specificity for each respective iCup drug class ranged from 89% (benzodiazepines) to 100% (amphetamines, barbiturates, buprenorphine, 3,4-methylenedioxymethamphetamine, methadone). Positive predictive values ranged from 44% (benzodiazepines) to 100% (amphetamines, barbiturates, buprenorphine, methylenedioxymethamphetamine, methadone), while negative predictive values ranged from 96% (methamphetamine) to 100% (barbiturates, methadone, tricyclic antidepressants). A high false-positive rate was yielded by the benzodiazepine class. The lack of fentanyl screening in the point-of-care device is a significant limitation considering its prolific prevalence in forensic casework. The results obtained in the study should be acknowledged when considering the use of the Alere iCup DX 14 in the context of postmortem casework to help indicate potential drug use contemporaneously with autopsy and when requiring such preliminary results prior to the release of a final forensic toxicology report.
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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 | 0.006 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".