Allergic Contact Dermatitis from Methyldibromoglutaronitrile
Bibliographic record
Abstract
BACKGROUND: Arriving at a diagnosis of allergic contact dermatitis is a multistep procedure including the establishing of contact allergy, demonstration of current exposure to the sensitizer, and assessment of clinical relevance. Sometimes, these steps are easy to get through; at other times, there may be problems with every step. OBJECTIVE: To demonstrate the possible difficulties and pitfalls in establishing the presence of contact allergy and diagnosing allergic contact dermatitis from exposure to the preservative methyldibromoglutaronitrile (MDBGN). METHODS: Simultaneous patch-testing with petrolatum preparations of MDBGN at various concentrations, use testing, and chemical analysis with high-performance liquid chromatography (HPLC). RESULTS: Contact allergy to MDBGN was established in two cases, with MDBGN in petrolatum at 0.5%. Results of HPLC investigation of moisturizers used by the patients and yielding positive results on patch and use tests disagreed with the information about preservatives on the labels of the moisturizers and with the Material Safety Data Sheets (MSDSs). CONCLUSIONS: Patch testing with MDBGN in petrolatum at a concentration of less than 0.5% may fail to diagnose a clinically relevant contact allergy. The information on labels of products, on MSDSs, and from manufacturers may not be reliable, which indicates the need for chemical analyses.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".