An exploratory quantitative risk assessment for high molecular weight sensitizers: wheat flour
Bibliographic record
Abstract
OBJECTIVE: Quantitative risk assessments have been made for wheat dust and allergen exposure and wheat sensitization using classical epidemiological approaches based on simple categorizations in exposure groups. Such analyses suggest the existence of an exposure threshold level for wheat specific sensitization and were used as input in recently conducted risk assessments for wheat flour by the American Conference of Governmental Industrial Hygienists and the Dutch Expert Committee on Occupational Standards. More advanced statistical analyses were applied using generalized additive modeling and smoothed plots to evaluate the shape of the exposure response relationship in greater detail and evaluate the presence of exposure thresholds. METHODS: Data were used from a recently conducted epidemiological study in bakery workers. Information was available on wheat sensitization (IgE antibodies), inhalable dust levels and wheat allergen levels. Initial analyses were based on simple exposure categorizations for inhalable dust and allergen exposure. A more detailed analysis using non-parametric generalized additive models (GAM models) and smoothing plots allowed inspection of the presence of an exposure threshold of evaluation of 'no' or 'lowest observed effect levels' (NOELs, LOELs) using exposure data on the individual level. RESULTS: All analyses showed an increasing sensitization risk with increasing exposure. The classical epidemiological analyses gave evidence for the existence of an exposure threshold or 'no observed effect level (NOEL)' for specific wheat sensitization between 0.5 and 1 mg/m3 of inhalable dust. The more advanced analyses did not suggest any evidence for the existence of an exposure threshold. However, estimates of a LOEL obtained by considering an arbitrary increase in sensitization risk between 1.5 and 2 as undesirable, were close to the NOEL from the classical analyses and would therefore not lead to an essentially different exposure limit. The criterion of an increase in wheat sensitization risk was based on the risk in non-wheat dust exposed populations. CONCLUSION: Exposure response modeling using different classical epidemiological approaches and advanced statistical methods resulted in health based LOEL or NOEL estimates within a relatively close range. But when sensitization accompanied by asthma or rhinitis symptoms was considered as critical endpoint, steeper exposure-response relationships were observed which would lead to lower LOEL values.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".