Measurement of lead, mercury, and cadmium in blood donors in Canada
Bibliographic record
Abstract
BACKGROUND: Fetal and neonatal exposure to lead is associated with irreversible adverse effects on neural development. There is no reliable threshold for lead effect, so limiting exposure is recommended. A significant correlation has been reported between post-transfusion blood lead level (BLL) in infants and lead levels in transfused RBC units. We measured levels of lead, mercury, and cadmium, in Canadian donor blood to investigate if concerning levels for neonatal transfusion exist. STUDY DESIGN AND METHODS: Whole blood samples from blood donors (n = 2529) were shipped cold within 7 days of donation. All permanent blood donation clinics across Canada were sampled. Twelve of these permanent clinics and 8 mobile clinics with a greater potential for having higher lead or mercury levels were oversampled. Heavy metals were measured by inductively coupled plasma mass spectrometry. RESULTS: Of all donations, 2.2% (lead) and 0.4% (mercury) had levels higher than the recommended thresholds for safe neonatal transfusion. BLLs were higher in males but there was no significant difference in the blood mercury levels of males versus females. Cadmium levels were higher in females. There was a positive correlation between donor age and levels of heavy metals, with lead having the strongest correlation (r = 0.47, p < .0001). Three clinics in close proximity to two lead-producing mines were among the clinics with the highest BLLs. Significantly higher blood mercury levels were observed in coastal clinics. CONCLUSION: Our data on donor blood heavy metal levels supports considering blood transfusion as an exposure source to heavy metals and encourages informed selection of blood units for transfusion to vulnerable groups.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".