Silicosis, asbestosis, and pulmonary fibrosis in Ontario, Canada from 1996 to 2019
Bibliographic record
Abstract
BACKGROUND: Silicosis is a fibrotic lung disease caused by exposure to respirable crystalline silica. Historically, silicosis was common among miners and other professions in the 20th century, and in recent decades has re-emerged in coal mining and appeared in new workplaces, including the manufacture of distressed jeans and artificial stone countertops. METHODS: Physician billing data for the province of Ontario between 1992 and 2019 were analyzed across six time-periods (1993-1995, 1996-2000, 2001-2005, 2006-2010, 2011-2015, and 2016-2019). The case definition was two or more billing records within 24 months with a silicosis diagnosis code (ICD-9 502, ICD-10 J62). Cases from 1993 to 1995 were excluded as prevalent cases. Crude incidence rates per 100,000 persons were calculated by time-period, age, sex, and region. Analyses were repeated in parallel for pulmonary fibrosis (PF) (ICD-9 515, ICD-10 J84) and asbestosis (ICD-9 501; ICD-10 J61). RESULTS: From 1996 to 2019, 444 cases of silicosis, 2719 cases of asbestosis and 59,228 cases of PF were identified. Silicosis rates decreased from 0.42 cases per 100,000 in 1996-2000 to 0.06 per 100,000 people in 2016-2019. A similar trend was observed for asbestosis (1.66 to 0.51 per 100,000 persons) but the incidence rate of PF increased from 11.6 to 33.9 per 100,000 persons. Incidence rates for all outcomes were higher among men and older adults. CONCLUSIONS: A decreasing incidence of silicosis was observed in this analysis. However, the incidence of PF increased, consistent with findings from other jurisdictions. While cases of silicosis have been recorded among artificial stone workers in Ontario these cases do not seem to have impacted the population rates thus far. Ongoing, periodic surveillance of occupational diseases is helpful for tracking population-level trends over time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".