Are We Reading Patch Test Reactions in a Uniform Way? An International Contact Dermatitis Research Group Study
Bibliographic record
Abstract
Abstract: Background: Concern was raised within the International Contact Dermatitis Research Group (ICDRG) regarding the scoring of weak allergic versus doubtful patch test reactions. Objective: To investigate the degree of uniformity in patch test reading. Methods: Five series of fictive contact dermatitis cases were written up by the study organizer and presented to expert participants. Each series was sent electronically to participants one by one. All dermatitis cases underwent patch testing, and the test result was a reaction characterized by erythema and infiltration. Within each case series, there were 5 subcases that differed only in the size of the test area showing erythema and infiltration. Three nearly identical case series had 1 crucial difference: the result of a repeated open application test (ROAT), both in the cases and controls. The experts had to determine whether the patch test reaction indicated contact allergy, defined as an immunologically acquired delayed hypersensitivity. All other reactions (negative, doubtful, and irritant) were classified as no contact allergy. Results: There was a big intra- and inter-individual variation in the patch test reading. Nobody read according to any of the 2 existing ICDRG classifications. The ROAT results sometimes influenced the scoring. Conclusion: A new ICDRG classification is needed.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".