O6D.1 Inflammatory markers in the plasma of firefighters heavily exposed to particulates
Bibliographic record
Abstract
Introduction In 2016 firefighters from Alberta, Canada deployed to a catastrophic fire in Fort McMurray. In the first few days, firefighters experienced heavy smoke exposures during greatly extended work shifts. Blood samples were collected to determine whether inflammatory markers might constitute a useful biomarker of exposure. In first responders exposed during the Word Trade Center disaster, inflammatory markers in serum samples collected within 6 months post-event were associated with poor recovery from exposure-related lung disorders. Methods Blood samples were collected at two fire services. At Service A, first samples were drawn two weeks from the start of the fire and second samples after 3–4 months. At Service B samples were collected over 4 weeks, starting 4 months from the first exposure. Samples were immediately centrifuged and the plasma stored at −80C before being evaluated for 42 cytokines or chemokines using a multiplex assay. A principal component analysis was carried out to reduce the number of correlated outcomes. Exposure to particulates was estimated for each firefighter using environmental PM2.5, total hours exposed, tasks carried out and the use of respiratory protection. Respiratory symptoms immediately before the fire, immediately post fire and at 4 months were collected using visual analogue (VA) scales. Results Inflammatory markers were assayed for 242 plasma samples from 175 firefighters. Six components were extracted of which only one, labelled the inflammatory marker component (IMC) was related to estimated exposure (p<0.001): values decreased with time since last exposure (p<0.001). All respiratory symptoms post-fire were greater in those with higher estimated PM2.5. IMC scores were independently related to cough and wheeze at 4 months, but the biomarker did not contribute to models for these endpoints that also included PM2.5. Conclusions Inflammatory markers were related to exposure but did not improve prediction of symptoms in the first months post fire.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".