Long-term outcomes of 0.1% tacrolimus eye drops in eyes with severe allergic conjunctival diseases
Bibliographic record
Abstract
BACKGROUND: Because atopic dermatitis does not heal completely, associated severe atopic keratoconjunctivitis (AKC) and vernal keratoconjunctivitis (VKC) often require long-term treatment. This study aims to evaluate the long-term outcomes of using 0.1% tacrolimus eye drops to treat these severe allergic conjunctival diseases. METHODS: Two-hundred-and-seventy eyes of 135 patients diagnosed with AKC or VKC from April 2004 to April 2014 were screened retrospectively. Patient demographics and objective signs were extracted from the electronic medical records. The severity of 10 objective signs, related to the palpebral and bulbar conjunctiva, limbus, and cornea, and intraocular pressure (IOP) were observed at baseline, at 2 weeks, 1, 2, 3, 6, and 12 months after starting treatment, and every 1 year thereafter (average use period: 8.4 ± 2.9 years). Safety was evaluated based on the incidence and severity of adverse events. RESULTS: 12 patients (AKC; 7 cases, VKC; 5 cases) who were treated with 0.1% tacrolimus eye drops were enrolled in this study. The total score of clinical signs significantly decreased after 2 weeks and remained effective thereafter. Tacrolimus eye drops elicited a statistically significant difference in the mean total clinical scores and IOP over the course of treatment (P < 0.001). Elevated IOP was observed in 2 cases and corneal infection in 1 case; these effects were completely controlled with medication. CONCLUSIONS: Topical tacrolimus may provide effective and long-term improvement in clinical signs of severe AKC and VKC cases that refractory to standard conventional treatment. TRIAL REGISTRATION: University Hospital Medical Information Network (UMIN) 000034460.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".