Cenegermin treatment for pediatric neurotrophic keratopathy: a systematic review
Bibliographic record
Abstract
OBJECTIVE: This systematic review evaluates the efficacy and safety of cenegermin in pediatric neurotrophic keratopathy (NK), assessing treatment outcomes, predictors of efficacy, and adverse effects. METHODS: Adhering to PRISMA guidelines, we reviewed studies from Embase, MEDLINE, HealthSTAR, and other databases up to July 28, 2024. Studies were included if they involved pediatric patients (≤18 years) treated with cenegermin for NK. Two reviewers independently screened and extracted data, assessing the risk of bias using Joanna Briggs Institute tools. RESULTS: Of the 20 articles reviewed, 8 case reports or series met inclusion criteria, encompassing 21 pediatric cases (33% female), with an average age of 5.87 ± 3.05 years. Etiologies included congenital conditions, radiotherapy, and infections. Cenegermin was administered as a 0.002% drop 6 times daily for 8 weeks. Treatment was typically initiated after the failure of conventional methods. Improvements in corneal transparency were observed in all cases, with visual acuity improved in 4 out of 6 studies reporting this metric. Factors associated with improvement in corneal transparency were the severity of preexisting corneal scarring and ocular surface instability. CONCLUSIONS: Cenegermin is a viable treatment option for pediatric NK, showing efficacy in improving corneal transparency and, to a lesser extent, visual acuity. However, its adoption is constrained by limited pediatric data, cost, and potential side effects. Further research is needed to establish long-term safety and effectiveness, and to optimize treatment protocols for this vulnerable population.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".