Contact Allergens in Prescription Topical Ophthalmic Medications
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Both active and inactive ingredients in topical ophthalmic agents may cause allergic contact dermatitis. Here, we examined ingredients in prescription topical ophthalmic medications available in the United States. METHODS: A comprehensive list of topical ophthalmic medications was generated using AccessPharmacy. Categories included antiglaucoma, antibiotic, antibiotic/corticosteroid, corticosteroid, antiviral, antifungal, mydriatic, and miotic agents. For each formulation, ingredients were investigated using the National Institutes of Health US National Library of Medicine database and/or manufacturer websites. Counts and proportions were calculated for inactive ingredients, including those in the American Contact Dermatitis Society (ACDS) Core 90 Allergen Series. RESULTS: Two hundred sixty-four unique prescription ophthalmic medications met the inclusion criteria. The most common ACDS Core 90 allergen/cross-reactor inactive ingredient was benzalkonium chloride (68.1%, 180/264), followed by sorbates (11.7%, 31/264), parabens (6.8%, 18/264), sodium metabisulfite (3.8%, 10/264), propylene glycol (3.0%, 8/264), and lanolin (3.0%, 8/264). Approximately 21% (20.8%, 55/264) of products had no ACDS Core 90 allergens/cross-reactor inactive ingredients. The most common ACDS Core 90 allergen/cross-reactor active ingredients were aminoglycoside antibiotics, bacitracin/polymyxin B, and corticosteroids. Important non-ACDS Core 90 allergens included inactive ingredients, such as EDTA 28.0% and thimerosal 2.7%, as well as active ingredients, especially β-blockers. CONCLUSIONS: Benzalkonium chloride, sodium metabisulfite, propylene glycol, and lanolin were common inactive ingredient allergens. Most ophthalmic categories had low allergen formulations available for patients with contact allergy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".