Rubber Accelerators in Medical Examination and Surgical Gloves
Bibliographic record
Abstract
BACKGROUND: Rubber accelerators play a significant role in glove-related occupational contact dermatitis, especially among health care workers. Currently, there is limited information readily available outlining the accelerators used in specific medical examination and surgical gloves. OBJECTIVE: The aim of this study was to ascertain the accelerators used in medical examination and surgical gloves for major glove manufacturers within the United States. METHODS: An initial Internet-based search was performed to establish relevant manufacturers and product lines, with subsequent inquiry with each corresponding company regarding accelerators used in each medical and surgical glove line. RESULTS: Eleven glove manufacturers were identified and contacted. Responses were obtained from all manufacturers, but because of legal limitations, changes in product lines, or inability to supply necessary data, only 8 companies were able to be included in the final analysis, totaling data for 190 gloves. Carbamates were the most common accelerator, used in 90.5% (172/190) of gloves, whereas thiurams were used in only 11 gloves (5.8%). Eight companies surveyed are now advertising and offering touted accelerator-free gloves. CONCLUSIONS: Accelerators are used in most examination and surgical gloves; however, manufacturers are now expanding their product offerings to include accelerator-free options.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".