A population‐based study on blood lead levels in blood donors
Bibliographic record
Abstract
BACKGROUND: Recent studies suggested that blood transfusion may represent a significant source of lead exposure in premature infants. Objectives of this study were to determine blood lead levels (BLLs) in a representative sample of blood donors and to identify risk factors associated with BLLs of 0.15 µmol/L or more. STUDY DESIGN AND METHODS: A study was conducted in 2006 to 2007 in 49 drive sites in Quebec. Individuals who qualified for blood donation were eligible to participate. Information was harvested from blood donor file and a standardized self-administered questionnaire. Lead analysis was performed by inductively coupled plasma mass spectrometry. Data on Quebec blood donors from 2003 to 2006 (n = 320,543) were used to establish a reference population. Geometric mean (GM) and 95% confidence interval (CI) were used to describe the results. The project was approved by an ethics committee. RESULTS: Of 6715 eligible individuals, 3490 participated (1392 women and 2098 men). Their mean age was 46.5 years. Results were weighted for region, sex, and age. The GM of BLLs was 0.082 µmol/L (95% CI, 0.027-0.247; range, 0.011-2.90 µmol/L). BLLs of more than 0.15 µmol/L were found in 15.5% of participants. In multivariate analysis, BLLs were mainly explained by age and sex of participants (p < 0.001). A significant association was also found between BLLs and the region of residence, education level, dwelling age, occupational and leisure activities at high risk for lead exposure, smoking, and alcohol intake (p < 0.001). CONCLUSION: BLL in blood donors is strongly explained by sex and age, a fact that can be taken into consideration when transfusing neonates.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".