Making Glove Decision Less of a White Knuckling Experience: A Systematic Review and Inventory of Glove Accelerator Contents
Bibliographic record
Abstract
BACKGROUND: Accelerators in medical gloves are a common cause of allergic contact dermatitis among healthcare workers. OBJECTIVE: A systematic review of medical and nursing literature, patch testing reports, and chemical analyses of gloves was conducted to assess accelerator contents reported in the literature and to identify accelerator-free gloves. METHODS: A systematic literature search was performed in OVID Medline and OVID EMBASE. Hand-searching of reference lists of articles in the field and author input generated the remainder of articles assessed. RESULTS: We present an inventory of accelerator contents of gloves and accelerator-free glove options as reported in the literature as a clinical reference tool to assist allergen-free glove selection for individuals suffering from allergic contact dermatitis due to rubber accelerators. LIMITATIONS: Pertinent limitations of our review include lack of predefined study exclusion criteria and screening of the studies identified in the search by 1 review author only. CONCLUSION: The glove inventory we provide summarizes the available literature regarding medical and surgical glove accelerator content, describing gloves both by brand and manufacturer as well as by accelerators.
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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".