USE OF LOW DOSE CHEST CT IN SILICA HEALTH SURVEILLANCE
Bibliographic record
Abstract
Background: Silicosis is one of the oldest occupational lung diseases with potentially fatal outcomes. In recent years, there has been an increase in the number of silicosis cases described in workers within the engineered stone industry. Despite this, the number of cases being detected in Western Australia up until 2020 remained low. This prompted WorkSafe WA to re-evaluate silica health surveillance processes specifically imaging modalities. Objectives: Low dose chest CTs were offered to a cohort of eligible workers within the industry to identify lung changes associated with the disease that may not be evident on chest radiographs. Methods: WorkSafe Western Australia offered fully-funded low dose chest CT scans to workers within the engineered stone industry who met the eligibility criteria. Results were reviewed and abnormal scans were discussed at multi-disciplinary meetings comprising of physicians from relevant medical specialities. Results: A total of 90 scans were carried out from July to November 2020. 8 workers within this cohort had changes consistent with silicosis on their scans. Of these 8, only 1 had an abnormal chest radiograph. 41 workers had findings consistent with early lung changes related to silicosis. These workers will require follow up and serial chest imaging moving forward. Only 1 worker within this cohort had an abnormal chest radiograph. Discussion: This project has highlighted the need to reconsider the use of chest radiograph in silica health surveillance. Findings from this project demonstrate benefits of low dose chest CTs in diagnosing silicosis and identifying early changes in the lung related to silicosis.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 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".