Validation of Dried Blood Spots for Maternal Biomonitoring of Nonessential Elements in an Artisanal and Small-Scale Gold Mining Area of Tanzania
Bibliographic record
Abstract
Abstract Biomonitoring studies of vulnerable populations in low- and middle-income countries are limited because traditional sampling methods are challenging to implement in low-resource settings. The present study examined the feasibility, precision, and accuracy of dried blood spots (DBS) for human biomonitoring of nonessential elements (cadmium [Cd], mercury [Hg], and lead [Pb]) in an area of northern Tanzania with artisanal and small-scale gold mining activities. Pregnant women (n = 44) were recruited in Geita during antenatal clinic visits, and DBS from capillary blood were collected on filter paper. As a gold-standard comparison, venous blood was sampled from the same participants and compared with the DBS. Venous blood, DBS, and quality control samples were analyzed for chemical elements by inductively coupled plasma mass spectrometry. Field blanks were very clean for most elements, generally only twice as high as corresponding laboratory filter blanks. No significant differences were found between duplicate DBS samples taken from the same participants, with near perfect intraclass correlation coefficients (0.99) for Cd, Hg, and Pb, indicating excellent reliability. Moreover, correlation was strong (r2 > 0.9) and significant (p < 0.0001) between DBS and the quantitative venous blood, with regression line slopes close to 1.0 (0.847, 0.976, and 0.969 for Cd, Hg, and Pb, respectively), indicating high accuracy of the DBS method compared with the gold-standard approach. The DBS method is minimally invasive and was a feasible, precise, and accurate means of measuring exposure to Cd, Hg, and Pb in pregnant women in a low-resource setting. Environ Toxicol Chem 2019;38:1285–1293. © 2019 SETAC
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".