Cross-Reactions between Xanthates and Rubber Additives
Bibliographic record
Abstract
BACKGROUND: We previously described allergic contact dermatitis from xanthates used in the recovery of metals from mining ores. We observed cross-reactions with carbamates, believed to be due to the common "dithio" nucleus shared by both groups. OBJECTIVE: The present study was undertaken to establish the rate of cross-reactions between xanthates and rubber additives. METHODS: Between November 2002 and December 2005, 1,220 consecutive patients were patch-tested with sodium isopropyl xanthate 10% in petrolatum (pet) and with potassium amyl xanthate 10% pet and later 5% pet, in addition to the North American Contact Dermatitis Group standard series and other series as required by their conditions. RESULTS: Fifty-one patients reacted to xanthates, carbamates, or thiurams; 26 reacted to xanthates only, and these reactions were felt to be irritant. Twenty-five patients reacted to xanthates and/or to one or more of the rubber additives, 12 had positive reactions to xanthates and to either carba mix or thiuram mix, 10 reacted to xanthates and carba mix, 9 reacted to xanthates and thiuram mix, and 8 showed positive reactions to xanthates and both mixes. However, 13 patients had positive reactions to carba mix and thiuram mix but did not react to xanthates. Six patients reacted to other rubber additives such as mercaptobenzothiazole, black rubber mix, and mixed dialkyl thioureas. Five of these patients also reacted to xanthates, 4 reacted to xanthates and carba mix, and 3 reacted to xanthates, carba mix, and thiuram mix. CONCLUSIONS: Of patients sensitized to carbamates, thiurams, or mercaptobenzothiazole, 50% exhibit cross-reactions with xanthates. Xanthates are irritants, and their patch-test concentrations should be lowered to 5% or less.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".