A Contemporary Fischer-Maibach Investigation
Bibliographic record
Abstract
BACKGROUND: Standardization of patch testing has been difficult to achieve. OBJECTIVES: This study aimed to identify physical variations in patch test systems that could affect delivery of allergens. METHODS: We compared the volume, depth, and contact area of 21 patch test delivery systems. We also filled a variety of patch test systems with different volumes of liquid (ferrous chloride) and petrolatum (disperse blue) allergens to investigate coverage and extrusion. RESULTS: The depth of chambers varied from minimal to greater than 1 mm. Mean areas ranged from 50 to almost 350 mm2. In most chambers, even the largest volume of liquid (40 μL) seemed to be completely contained by each product's absorbent material. The amount of petrolatum required to provide 100% coverage ranged from 15 to 45 μL. The dose delivered, as defined by mg per cm2, varied more than 2-fold across the systems. CONCLUSIONS: There are considerable differences across various patch tests. Different patch test systems likely do not deliver the same dose of allergen if the same volume of excipient is applied. Appreciating the differences between different patch test systems may help refine recommendations for the amount of allergens that should be applied to different patch test systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.014 |
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".