Qualitative thallium urinary assays are almost as valuable as quantitative tests: implication for outpatient settings in low and middle income countries
Bibliographic record
Abstract
BACKGROUND: Thallium (Tl), lead and steroid exposures were reported as a result of street drug consumption such as heroin and cocaine. OBJECTIVE: This study aimed to compare the values of qualitative and quantitative assays for detecting Tl as an adulterant in opioid-like compound drug users. METHODS: This case-control study was conducted throughout a specified time bracket ranging from May 2015 to November 2015 in Imam Reza Hospital, Mashhad, Iran. In general, urine thallium samples for 100 opioid overdosed subjects with a history of chronic opioid use and 50 non-drug users were studied. Qualitative 24 hours-urinary-thallium-level (QL) and quantitative 24 hours-urinary-thallium-level (QT) were conducted in both groups. Independent-samples t-test and Spearman's Coefficient were applied for analytical purposes. SPSS software 16 was used to conduct statistical analyses with P values less than 0.05 regarded as significant. RESULTS: A total of 150 cases were studied. Raw opium users accounted for 66% of the cases followed by mixed (28%) and heroin users (6%). Mean (SD) QT level for QL was 26.8 (1) μg/L, while it was 2.3 μg/L (0.4 μg/L) for negative QL, which was far below QL positive cases (p=0.002). The qualitative test showed more accuracy at higher quantitative levels. In all cases, qualitative test was fully sensitive (100%), highly specific (89%) with a positive likelihood ratio (PLR) of 9.1 and a negative likelihood ratio (NLR) of 0. CONCLUSION: These results suggest that qualitative assays could be used with confidence in assessing Tl exposure in drug users. Physicians may easily and confidently use Tl qualitative tests in rehabilitation centers, where toxicology laboratories may not be available.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".