Evaluation of a second‐generation portable blood lead analyzer in an occupational setting
Bibliographic record
Abstract
BACKGROUND: A new blood lead testing instrument has qualities that make the instrument attractive for on-site testing of occupational lead exposures. This study evaluated the accuracy of the instrument when used in a manufacturing setting, and examined the impact of blood storage and shipment on results. METHODS: Venous blood specimens (n=121) were obtained and immediately analyzed on-site using the new instrument. They were then shipped to a reference laboratory and analyzed using electro-thermal atomization atomic absorption spectrometry (ETAAS), and retested using the new instrument. RESULTS: The cohort blood lead concentration averaged 40.1 microg/dl. Results obtained on the new analyzer with freshly collected blood averaged 38.7 microg/dl. The mean difference of 1.2 microg/dl on paired samples was not statistically significant. Following blood shipment and storage, results on the analyzer increased to an average of 42.4 microg/dl. The mean increase of 3.0 microg/dl on stored blood samples also failed to reach statistical significance. Under OSHA proficiency test acceptability requirements, 94% of the results had satisfactory agreement. CONCLUSIONS: The new analyzer might be a useful tool for on-site monitoring of occupational lead exposures. The manufacturer's instructions should be adhered to with respect to specimen age and storage requirements.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".