Phenotypic Spectrum of Peters Anomaly: Implications for Management
Bibliographic record
Abstract
Purpose: The aim of this study was to characterize the wide phenotypic spectrum of Peters anomaly and to suggest a management algorithm based on disease phenotype. Methods: The charts of all children diagnosed with Peters anomaly between January 2000 and December 2013 were reviewed retrospectively. Anterior segment color photographs, anterior segment optical coherence tomography, and ultrasound biomicroscopy images were used to phenotype disease severity and to guide management. Disease severity was categorized to Peters anomaly type I and II according to lens involvement. Peters anomaly type I and II were further categorized from mild to severe disease according to the size and location of corneal opacity. Associated systemic findings were also documented. Results: Eighty eyes of 54 patients with Peters anomaly were identified, of which 28 (51.9%) had unilateral disease. Peters anomaly type I was present in 40 patients (57 eyes, 71.2%) and Peters anomaly type II in 14 patients (23 eyes, 28.8%). Nine eyes (11.3%) had phenotypic features that required observation only, 24 eyes (30%) were amenable to pupillary dilation, 43 eyes (53.8%) with large, dense central opacity required penetrating keratoplasty, and 4 eyes (5.0%) had no intervention because of very poor prognostic features. Associated systemic abnormalities occurred frequently in Peters anomaly (n = 20, 37.0%), with congenital heart defect being the most common morbidity (n = 10, 18.5%). Conclusions: Peters anomaly presents with a variable phenotype ranging from minimal peripheral corneal opacity to extensive iris and lens adhesions with dense central corneal opacity detrimental to vision. Management can be standardized and guided by an algorithm based on phenotypic severity. Systemic abnormalities should be ruled out, regardless of the severity of Peters anomaly.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".