Incidence and outcomes of microbial keratitis after cyclophotocoagulation to treat childhood refractory glaucoma
Bibliographic record
Abstract
PURPOSE: To report the incidence and outcomes of microbial keratitis (MK) following cyclophotocoagulation (CPC) for treatment of refractory childhood glaucoma (CG) at a single center over a period of 6 years. METHODS: In this cohort study, the medical records of children with CG who underwent CPC and subsequently presented with MK from 2014 to 2020 were reviewed retrospectively. Data were collected on age, type of glaucoma, surgeries before MK, CPC parameters, interval between CPC and MK, presenting symptoms of MK, infiltrate location, bacterial isolates, MK treatment, and outcomes. RESULTS: Among the 312 children who underwent CPC during the study period, 37 eyes of 33 children had MK, with an incidence of 1.8% (95% CI, 0.3-3.2). The median interval between CPC and MK was 4 years (IQR, 2.7-7.4). CPC was repeated once in 20 eyes (54%) and twice in 4 (11%). In 20 eyes, there was no pain at MK onset. The primary isolates were Streptococcus pneumoniae (12/27 [22%]) and Staphylococcus epidermis (8/27 [30%]). MK resolved in 17 eyes (46%) after treatment; 8 eyes (22%) underwent evisceration or had phthisis, and keratoplasty failed in 6 eyes (16%). The absence of pain at presentation with MK was negatively associated with resolution (OR = 5.0 [95% CI, 1.1-23.8]; P = 0.04). CONCLUSIONS: The absence of pain at MK onset may be a proxy for neurotrophic keratitis after CPC and is linked to poor response to management.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".