Retrospective Exposure Assessment for Occupational Disease of an Individual Worker Using an Exposure Database and Trend Analysis
Bibliographic record
Abstract
This article outlines a hierarchy of data required for retrospective exposure assessment for occupational disease of an individual worker. It then outlines in a step-wise manner how trend analysis using a relatively large exposure database can be used to estimate such exposure. The process of how a large database containing exposure measurements can be prepared for estimating historic occupational exposures of individual workers in relation to their illnesses is described. The asbestos subset from a large government collected air monitoring database called Medical Surveillance (MESU) was selected to illustrate the cleaning and analysis processes. After unidentifiable values were removed, the cleaned dataset was examined for possible sources of variability such as changes to sampling protocol. Limit of detection (LOD) values were substituted for all non-detectable values prior to the calculation of descriptive statistic using left censored analysis methods (i.e., maximum likelihood estimation (MLE), Kaplan Meier (KM), and simple substitution). The JoinPoint Regression Program was used to perform trend analysis and calculate an annual percentage change (APC) value for the available sampling period. An asbestos case study is presented to illustrate how the APC can then be combined with more recent job and/or process specific exposure data to estimate historic levels. The MESU asbestos dataset contained 1,610 samples from 1984-1995. An average of 17% of this data was left censored. The asbestos air sampling methods in Ontario changed around 1990. LOD values of 0.06 f/cc and 0.02 f/cc were substituted for LOD values pre- and post-1990, respectively. The annual mean fiber levels for the MLE method were an average of 44% lower than KM and substitution methods. The corresponding APC for MLE method was -6.5% and -7.7% for KM and simple substitution. The findings of this paper illustrate how the temporal trend of an exposure databases can be used to efficiently estimate historic contaminant levels in the presence of limited historical information.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".