Finding toxicological information: An approach for occupational health professionals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: It can be difficult for occupational health professionals to assess which toxicological databases available on the Internet are the most useful for answering their questions. Therefore we evaluated toxicological databases for their ability to answer practical questions about exposure and prevention. We also propose recommended practices for searching for toxicological properties of chemicals. METHODS: We used a systematic search to find databases available on the Internet. Our criteria for the databases were the following: has a search engine, includes factual information on toxic and hazardous chemicals harmful for human health, and is free of charge. We developed both a qualitative and a quantitative rating method, which was used by four independent assessors to determine appropriateness, the quality of content, and ease of use of the database. Final ratings were based on a consensus of at least two evaluators. RESULTS: Out of 822 results we found 21 databases that met our inclusion criteria. Out of these 21 databases 14 are administered in the US, five in Europe, one in Australia, and one in Canada. Nine are administered by a governmental organization. No database achieved the maximum score of 27. The databases GESTIS, ESIS, Hazardous Substances Data Bank, TOXNET and NIOSH Pocket Guide to Chemical Hazards all scored more than 20 points. The following approach was developed for occupational health professionals searching for the toxicological properties of chemicals: start with the identity of the chemical; then search for health hazards, exposure route and measurement; next the limit values; and finally look for the preventive measures. CONCLUSION: A rating system of toxicological databases to assess their value for occupational health professionals discriminated well between databases in terms of their appropriateness, quality of information, and ease of use. Several American and European databases yielded high scores and provide a valuable source for occupational health professionals.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 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) | 0.001 | 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 it