Assessing the utility of drug screening in the emergency: a short report
Bibliographic record
Abstract
Exposure to illicit drugs and alcohol is a major cause for visits to the emergency department (ED).1 For most drugs of abuse intoxication, ED physicians are sceptical to rely on the results of drug screens because immunoassays, although rapid and relatively cheap, have limitations in their sensitivity and specificity, and also carry relatively high rates of false positives and negatives.2 However, as it is often difficult to obtain the history from intoxicated patients, drug screens are still frequently ordered. With the emergence of Choosing Wisely, clinicians are becoming increasingly aware of the need to reduce the ordering of unnecessary tests.3 In this retrospective study, we explored the utility of drug screening in an acute care hospital ED to determine the frequency, patterns, indication and impact of drug screening for patients presenting with a mental health or addiction (MHA) chief complaint. Ethics approval was granted by the institutional review board of the primary research site. The Strengthening the Reporting of Observational Studies in Epidemiology guidelines for observational studies were followed.4 The charts of patients seen in the ED of a local hospital with an MHA chief complaint …
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".