Fire-Retardant Clothing–Related Dermatitis
Bibliographic record
Abstract
BACKGROUND: Several laborers in a refinery unit undergoing a work over noted skin eruptions. Signs and symptoms included erythema, pruritus, scaling, and perifollicular inflammation on skin contacted by fire-retardant clothing (FRC). OBJECTIVE: The purposes of this report were to show a correlation between this rash outbreak and the use of FRC, to report the investigative results as to what aspect of the FRC most likely caused the dermatitis, and to present how this outbreak was ended. METHODS: Employees received questionnaires, were examined, and received patch testing, and pH testing of FRC was performed to evaluate the causative factors. RESULTS: More than 100 workers reported a rash, and approximately a third of these individuals exhibited a unique rash. There was a trend toward Hispanic and white workers being more affected than black workers. The onset of the rash peaked from June to August. This FRC-related rash resolved with the use of manufacturer-recommended laundering procedures. CONCLUSIONS: The FRC-associated eruption was most likely a form of irritant contact dermatitis due to inadequate laundering procedures. The most effective preventative measure other than proper laundering was the use of underclothing to prevent contact of FRC with sweat-moistened skin.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".