Inflammation related to inhalation of nano and micron sized iron oxides: a systematic review
Bibliographic record
Abstract
Inhalation exposure to iron oxide occurs in many workplaces and respirable aerosols occur during thermal processes (e.g. welding, casting) or during abrasion of iron and steel products (e.g. cutting, grinding, machining, polishing, sanding) or during handling of iron oxide pigments. There is limited evidence of adverse effects in humans specifically linked to inhalation of iron oxides. This contrasts to oxides of other metals used to alloy or for coating of steel and iron of which several have been classified as being hazardous by international and national agencies. Such metal oxides are often present in the air at workplaces. In general, iron oxides might therefore be regarded as low-toxicity, low-solubility (LTLS) particles, and are often considered to be nontoxic even if very high and prolonged inhalation exposures might result in diseases. In animal studies, such exposures lead to cancer, fibrosis and other diseases. Our hypothesis was that pulmonary-workplace exposure during manufacture and handling of SPION preparations might be harmful. We therefore conducted a systematic review of the relevant literature to understand how iron oxides deposited in the lung are related to acute and subchronic pulmonary inflammation. We included one human and several in vivo animal studies published up to February 2023. We found 25 relevant studies that were useful for deriving occupational exposure limits (OEL) for iron oxides based on an inflammatory reaction. Our review of the scientific literature indicates that lowering of health-based occupational exposure limits might be considered.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".