Allergen Content of Patient Problem and Nonproblem Gloves: Relationship to Allergen-Specific Patch-Test Findings
Bibliographic record
Abstract
BACKGROUND: Identification of putative contact allergen and source material is often done by a combination of patch testing and manufacturer-supplied product information. The accuracy of the identification of allergen-source material and level of allergen in that allergen-source material is not known. OBJECTIVE: The objectives of the study were to survey the chemical allergen content of glove allergic contact dermatitis (ACD) patient-identified problem and nonproblem gloves and to evaluate the ability of the patient to discriminate between problem and nonproblem gloves. METHODS: Gloves from patch-tested rubber allergen-positive ACD patients were analyzed for species and amount of rubber allergen. RESULTS: Approximately half the subjects were able to correctly identify their problem and nonproblem gloves. Correct association of a glove with ACD was directly related to patch-test reaction severity and inversely related to the number of glove brands being used by the patient. Of note, thiurams were not detected in any of the gloves examined. CONCLUSIONS: Although patch testing is invaluable in identifying individual allergen sensitivities, the identification of the ACD-causative specific chemical allergen and source material remains problematic. All glove brands used within days prior to and during an ACD episode should be considered potential sources of the contact allergen.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".