Evaluation of the Drug Evaluation and Classification Program: A Critical Review of the Evidence
Bibliographic record
Abstract
OBJECTIVE: A critical review of the existing evaluation studies on the Drug Evaluation and Classification (DEC) program was conducted to determine the validity and accuracy of the technique for identifying drivers under the influence of drugs. METHODS: Studies were divided into two categories--laboratory studies and field (i.e., enforcement) studies. A classification process was devised using common criteria based on the toxicology findings (i.e., drug positive or drug negative) and the opinion of the police officer who assessed the driver (i.e., drug positive or drug negative). A series of standard measures (Sensitivity, Specificity, False Alarm Rate, Miss Rate, Corroboration, and Accuracy) were calculated for each to assess the effectiveness of the DEC program. RESULTS: Laboratory studies do not provide overwhelming support for the accuracy with which officers trained in the DEC program can detect and identify the particular class(es) of drug involved based on psychophysical assessment alone. The detection and identification of the relatively low levels of drugs administered were typically better than chance but many cases were missed. The fact that some drugs were detected with greater accuracy than others suggests that the effects of these substances were more prominently manifested in the symptomology assessed by the DEC procedure. Although field enforcement studies are not as scientifically rigorous as laboratory studies, DEC assessments in an enforcement context have the benefit of information obtained from the arresting officer and from interviews with the suspect. In addition, the drug doses consumed by users are typically much higher than those permitted in controlled laboratory studies. In general, officers trained in the DEC program are able to identify persons under the influence of drugs and to specify the drug class responsible with a degree of accuracy that not only exceeds chance, but in some cases reaches a very high level. CONCLUSIONS: There remains room for improvement in the DEC program. As further research becomes available, either from laboratory or field investigations or both, it needs to be incorporated into the program to enhance its validity and accuracy.
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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 | 0.027 | 0.004 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".