Are material safety data sheets (MSDS) useful in the diagnosis and management of occupational contact dermatitis?
Bibliographic record
Abstract
OBJECTIVES: This study assesses both the success of medical practitioners in accessing hazardous substances' information from product manufacturers and the accuracy and clinical usefulness of Material Safety Data Sheets (MSDS) presented by workers with suspected occupational contact dermatitis (OCD). PATIENTS/METHODS: 100 consecutively presented MSDS were collected from 42 workers attending an occupational dermatology clinic. Product manufacturers were contacted to verify ingredients. MSDS were evaluated for compliance with the Australian criteria for listing of OCD relevant information (sensitizers present at a concentration > or =1%, irritants present at a concentration > or =20%), and for clinical usefulness. All sensitizers were checked for clinical relevance to the worker's dermatitis. RESULTS: Manufacturers supplied product constituents for 77/100 MSDS. 58 MSDS satisfied the Australian standard. 57/58 MSDS were deemed clinically useful. Irritants were listed for 19/23 MSDS and sensitizers were listed for 30/68 MSDS (P = 0.001). 3 MSDS contained sensitizers, which were clinically relevant to the presenting worker's dermatitis, 1 appropriately listed, 1 present at > or =1% but not listed, and 1 present at <1% in the product and therefore, not required to be listed. CONCLUSIONS: Sensitizers are frequently omitted from MSDS and clinicians are often unsuccessful in obtaining crucial information from manufacturers. MSDS are inadequate for the protection and diagnosis of workers with suspected OCD.
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 | 0.019 | 0.152 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".