Accessing Biobanks to Obtain Human Biomonitoring Data
Bibliographic record
Abstract
Background: The use of human biomonitoring data allows direct and more precise assessment of the distribution of exposure in a given population. In Canada, biomonitoring data in the general population has been continuously measured since 2007, by the Canadian Health Measures Survey (CHMS). Integrated within the CHMS, the biobank is a nationally representative cohort to facilitate novel health research.Objective: Some of the Chemical Management Plan’s priority substances; Al, Bi, Ce, Cr, Ge, La, Li, Nd, Pr, Ti, Te and Y were not included in the CHMS. After obtaining approval from relevant authorities, approx. 6000 whole blood samples from the CHMS biobank were accessed to determine metal concentrations by Inductively Coupled Plasma Spectrometry.Method: Resultant concentrations were expected to be at ppt or ppb levels. The main challenges were: possible leaching of metals from storage and associated materials, non-homogeneity of blood samples, transportation of samples and analytical issues related to testing a large batch of samples in the absence of data for comparison. Leaching was addressed by sequential testing of de-ionized water and blood, mimicking the procedure from the withdrawal of the blood samples, storage and analysis. Non-homogeneity of the samples and possible contamination during analysis were addressed by reproducibility studies. Throughout the project (1.5 years), accuracy, recovery, reproducibility, and contamination were assessed using Certified Blood Reference Materials, a control spiked blood sample, and Method Blanks along with each analytical batch.Results: Recoveries were in an acceptable range of 70-130%; the majority between 80-120%, with an inter assay CV% ranging from 3-11%. Leach testing showed no contamination from the storage or associated materials used for Bi, Ce, Cr, Ge, La, Li, Nd, Pr, Te, except for Y, which had minor contamination close to the detection limit from materials associated with blood withdrawal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.038 |
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".