<i>CYP1B1</i>and<i>MYOC</i>variants in neonatal-onset versus infantile-onset primary congenital glaucoma
Bibliographic record
Abstract
Objective To compareCYP1B1andMYOCvariants in a cohort of neonatal-onset (NO) and infantile-onset (IO) primary congenital glaucoma (PCG). Methods This prospective observational study included 43 infants with PCG (14 NO and 29 IO) presenting between January 2017 and January 2019 with a minimum 1-year follow-up.CYP1B1andMYOCgenes were screened using Sanger sequencing with in-silico analysis of the variants using Polymorphism Phenotyping v.2 and Protein Variation Effect Analyser platforms. Allelic frequency was estimated using Genome Aggregation Database (gnomAd). Disease presentation and outcome were correlated to the genetic variants in both groups. Results Babies withCYP1B1mutations had more severe disease at presentation and worse outcomes. Six of 14 (42.8%) NO glaucoma and 5 of 29 (17.2%) IO harbouredCYP1B1mutations. Five of six babies in the NO group and three of five in the IO group harboured the variant c.1169G>A, [p.R390H]. They required more surgeries and had a poorer outcome. On in-silico analysis c.1169G>A, [p.R390H] scored very likely pathogenic. Two patients in the IO group who had the c.1294C>G, [p.L432V] variant had a good outcome. Five of 14 NO-PCG and 8 of 29 IO-PCG harboured the variant c.227G>A, [p.R76K] in theMYOCgene, which was scored benign by in-silico analysis, and was also found in 2 of 15 normal controls. Conclusions Patients withCYP1B1pathogenic variants had a poorer outcome than those without. We found more NO PCG babies withCYP1B1mutations compared with IO PCG. This may be one of the reasons for NO PCG having a poorer prognosis compared with IO PCG.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".