Screening Workers for Occupational Exposure to Respirable Crystalline Silica: Development and Usability of an Electronic Data Capture Tool
Bibliographic record
Abstract
Background: Cases of the occupational lung disease silicosis have been identified in workers processing artificial stone in the stone benchtop industry (SBI). In the Australian state of Victoria, the Regulator commissioned a screening program for all workers in this industry. Objective: To facilitate systematic data collection, including high-quality exposure assessment, an electronic data capture tool (EDCT) was developed. Methods: A multidisciplinary team developed an EDCT using Research Electronic Data Capture (REDCap; Vanderbilt University). The needs of the EDCT were (1) data entry by multiple clinicians and the workers attending for screening and (2) systematic collection of data for clinical and research purposes. The comprehensibility and utility of the tool were investigated with a sample of workers, and the EDCT was subsequently refined. Results: The EDCT was used in clinical practice, with capacity for data extraction for research. Testing of comprehension and utility was undertaken with 15 workers, and the refined version of the Occupational Silica Exposure Assessment Tool (OSEAT) was subsequently developed. Conclusions: The refined OSEAT has been determined to be comprehensible to workers and capable of collecting exposure data suitable for assessment of risk of silicosis. It was developed for workers in the SBI in Australia and is adaptable, including translation into other languages. It can also be modified for SBI workers in other countries and for use by workers from other industries (mining, construction) at risk of silica exposure, including in lower-income settings.
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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 | 0.053 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".