Prevalence of Contact Allergens in Best-Selling Ophthalmic Products
Bibliographic record
Abstract
BACKGROUND: Ophthalmic products are a common but often overlooked contributor to allergic contact dermatitis. Frequency of allergenic ingredients in over-the-counter ophthalmic products has not been well characterized. OBJECTIVE: The purpose of this study was to determine the prevalence of allergenic ingredients in most commonly bought eye lubricants and contact lens solutions. METHODS: A product list of Amazon.com's best-selling ophthalmic products was curated by searching for "Best Sellers in Eye Drops, Lubricants & Washes" and "Best Sellers in Contact Lens Care Products." For exploratory analysis, indication, price, consumer ratings, number of reviews, and US Food and Drug Administration approval status were recorded. The products' ingredients were compiled using NLM DailyMed, and products that had 1 or more allergens or relevant cross-reactors on either the 2018 American Contact Dermatitis Society Core Allergen Series or the 2015-2016 North American Contact Dermatitis Group Standard Allergen Series were noted. RESULTS: Forty-eight percent (n = 49) of the total products, (57.8% [n = 37] of eye lubricants, and 31.6% [n = 12] of contact lens solutions) had 1 or more allergens or associated cross-reactors. Identified allergens were benzalkonium chloride, propylene glycol, sorbic acid, amidoamine, sorbitan sesquioleate, chlorhexidine digluconate, lanolin alcohol, parabens, benzyl alcohol, and butylated hydroxytoluene. CONCLUSIONS: Awareness of potential allergens is crucial to diagnosing allergic contact dermatitis to ophthalmic products and helping patients navigate online pharmacological chaos.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".