The Value of Patch Testing With Shoe Material Samples in Patients Suspected of Shoe Allergic Contact Dermatitis
Bibliographic record
Abstract
BACKGROUND: Patch testing is the standard diagnostic tool for shoe allergic contact dermatitis (ACD). As shoe materials change over time, utilizing commercial allergen series might be ineffective. However, because testing with patients' shoe samples is laborious, its value is questioned. OBJECTIVE: The aim of this study was to ascertain the benefits of patch testing with patients' shoes by comparing the frequencies of patch-test positivity of shoes and shoe-related allergens in baseline series for suspected shoe ACD patients. METHODS: A retrospective study was conducted of patients with clinically suspected shoe ACD who underwent patch testing with baseline series and shoe samples 2000 to 2019. RESULTS: Almost half of the cohort (77 of 178; 43.3%) was diagnosed with shoe ACD. Of those 77, 48 (62.3%) were positive to shoe-related allergens from the baseline series, whereas 53 (68.8%) were positive to their shoe materials. The prevalence of tests positive to shoe material but negative to shoe-related allergens was 29 of 77 (37.7%). The most common shoe-related allergens were potassium dichromate (7.9%), carba mix (6.8%), and mercapto mix (6.7%). CONCLUSIONS: Patch testing with shoe materials increased the diagnostic yield by 37.7 percent. To diagnose shoe ACD, testing of shoe materials may compensate for unknown, scarce, or novel allergens not in the baseline 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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".