Assessment of Contact Allergens in “Hypoallergenic” Athletic Shoes by Mass Spectrometry
Bibliographic record
Abstract
Abstract: Background: Identification of athletic shoes for patients with contact allergy is difficult. Company reports of allergen content are often incorrect. Objectives: To determine whether chemical analysis of 4 athletic shoes, previously reported to be free of the most common contact allergens, contain quantifiable allergen levels. Methods: Samples from the uppers and insoles of 4 shoes believed to be free of common allergens were assessed by mass spectrometry. A total of 4 rubber accelerators and 2 adhesives were directly quantified and additional 7 rubber accelerators were assessed using semiquantitative measures. Results: Aside from carbamates (assayed as 59 ppm zinc in insoles) in SeaVee's Sixty-Six sneakers, para-tertiarybutylphenol formaldehyde resin (PTBFR) (assayed as 7.6 ppm paratertiary butylphenol or 4-tertiary butylphenol [4TBP] in uppers) in Allbirds Tree Runners and rosin (assayed as 628 ppm sodium abietate in uppers) and carbamates (24 ppm zinc in uppers) in Saucony Jazz sneakers, these shoes had low levels of all allergens assayed in this study. Tom's Carlo sneakers contained rosin (127 ppm sodium abietate in insoles), PTBFR (6.5 ppm 4TBP in uppers), and carbamates (112 ppm sodium abietate in insoles) but had low levels of all other assayed allergens. Conclusions: Although identifying allergen-free shoes is challenging, the results of this analysis will help patch testing physicians recommend athletic shoes to patients with specific allergies.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".