The benefit of scratch patch testing to demonstrate ocular contact allergy to brimonidine tartrate
Bibliographic record
Abstract
INTRODUCTION: Ocular allergies to brimonidine are frequent in patients treated for glaucoma. There is variability in reporting due to the lack of diagnostic criteria and the absence of cutaneous testing. Many false-negative patch tests (PT) have been described. Alternative methods, such as strip and scratch PT, have been used without a standardized method. OBJECTIVES: The primary objective is to identify the best method of cutaneous testing and brimonidine concentration for patch testing. The secondary objective is to identify clinical signs and symptoms suggestive of ocular allergy. PATIENTS AND METHODS: A retrospective review of patient files suspected of brimonidine ocular allergy was performed. Patch testing method, brimonidine concentration and clinical symptoms were reviewed. RESULTS: Of the 36 patients identified, half tested positive for brimonidine for at least one of the testing methods. The scratch PT demonstrated 17 positive reactions (94% detection rate). Three patients reacted with strip PT. No positive results were found with standard PT. The 5% brimonidine concentration demonstrated the highest sensitivity. The absence of eyelid pruritus was associated with negative testing. CONCLUSION: In the investigation of ocular allergy to brimonidine, scratch PT proved to be an essential tool. Brimonidine 5% pet. appeared as the most sensitive concentration for scratch PT.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".