Concomitant Patch Test Reactions to Mercapto Mix and Mercaptobenzothiazole
Bibliographic record
Abstract
BACKGROUND: Mercaptobenzothiazole (MBT) and mercapto compounds are primarily used in rubber products. OBJECTIVE: This study aimed to examine concomitant-positive rates of MBT (1% pet) and the 4-part mercapto mix (MM) (1% pet). DESIGN: This is a retrospective cross-sectional data from the North American Contact Dermatitis Group. RESULTS: A total of 30,880 patients were patch tested to MM and MBT. There were 333 positive reactions to MM and 427 positive reactions to MBT. Ninety-eight patients were positive to MM alone, 192 to MBT alone, and 235 reacted to both. Forty-five percent (192/427) of MBT reactions would have been missed by only testing to MM, and 29% (98/333) of MM reactions would have been missed by testing to MBT alone. Most of these "missed" reactions, however, were doubtful (+/-) or mild (+) (MBT, 65%; MM, 78%), whereas most reactions in patients who reacted to both were moderate (++) and/or strong (+++) (52.3%). Gloves were the most common source. CONCLUSIONS: Mercaptobenzothiazole is the preferential screening allergen for mercapto compounds because of the following: (1) greater proportion of missed reactions with MM; (2) greater proportion of doubtful/mild reactions in the missed group for MM; and (3) in the group positive to both, the low rate (2%) of moderate/strong reactions to MM and doubtful/mild reactions to MBT as compared with the converse (21%). Mercapto mix may be useful in an auxiliary rubber series.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".