Idiopathic Pulmonary Fibrosis: What do we Know about the Role of Occupational and Environmental Determinants? A Systematic Literature Review and Meta-Analysis
Bibliographic record
Abstract
The objectives of this systematic review of original articles published up until August 2021 and meta-analyses were to identify the links between occupational and non-occupational environmental exposures, types of occupations and idiopathic pulmonary fibrosis (IPF). Sixteen selected case-control studies were qualified as good level with Newcastle-Ottawa quality assessment scale. Sensitivity analyses highlighted the role of choice of control group, tobacco adjustment and diagnostic tools. Significantly increased risks of IPF were observed (OR (95%CI): for metals (1.42(1.05–1.92)), wood (OR:1.32(1.02–1.71)), and general dust (OR:1.32(1.08–1.63)) exposures. Subgroup analyses found a significantly elevated risk for: hardwood (OR:1.75 (1.13–2.70)), organic dusts (OR:1.72 (1.20–2.46)) and pesticides (OR:2.30 (1.30–4.08)), while no significant change was noted for softwoods and solvents. Smoking adjustments: general dust (1.45 (1.04–2.03)/organic dust (2.5 (1.49–4.22)/metals (1.87 (1.16–3)/wood dust OR: 1.16 (0.86–1.61)/pesticide exposure 2.4 (0.84–6.9) were calculated. Among agricultural workers, the risk was also increased (OR:2.06 (1.02–4.16)). Few environmental data were available and no significant associations detected. Thus, these meta-analyses highlighted the role of some occupational exposures in IPF occurrence. A more accurate and thorough assessment of exposures over the entire working life as well as on the duration and intensity of exposure and complex of multi-pollutant exposure is needed in future research and clinical practice.
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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 | 0.029 | 0.071 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.032 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".