Human biomonitoring health-based guidance values: A case study of the HB2GV Dashboard and DEHP
Bibliographic record
Abstract
In 2022, the International Society of Exposure Science (ISES) International Human Biomonitoring (i-HBM) Working Group launched a free, online repository of biomonitoring guidance values referred to as the Human Biomonitoring Health-Based Guidance Value (HB2GV) Dashboard. The goal of the Dashboard is to assist global human biomonitoring data users (e.g., risk assessors, risk managers) and human biomonitoring programs with a readily available compilation of guidance values for the general population. The Dashboard contains approximately 600 HB2GVs for over 150 chemicals or their metabolites. Although there are many different types of HB2GVs, most are Biomonitoring Equivalents (BEs), Human Biomonitoring (HBM-I and HBM-II) values, or Human Biomonitoring Guidance Values (HBM-GVs). For users new to human biomonitoring, understanding how the different types of HB2GVs are derived and how to interpret those values in the context of human biomonitoring data can be difficult. Therefore, there is a need to inform users of the differences among available guidance values and to help users identify the HB2GV that could be most suitable for their purposes. Here, we summarize the derivation of HB2GVs for a case study chemical, di-(2-ethylhexyl) phthalate (DEHP). We selected DEHP as there are 36 unique HB2GVs available from three of the most common types of guidance values (i.e., BE, HBM-I value, HBM-GV). We also compare the available HB2GVs with a focus on the differences among their derivation methods, relative quality and confidence, and interpretation. This case study provides guidance on the use of existing HB2GVs for health-based interpretation of human biomonitoring data that may be applied to other chemicals. As with any other type of guidance or regulatory value (e.g., RfDs, MRLs), thoughtful selection and use are strongly encouraged. Appropriately interpreting HBM data with the aid of guidance values can result in improved decision making which, ultimately, could lead to better protection of public health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 teacher head, 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".