Retinopathy of Prematurity (ROP) in high-risk babies and laser treatment
Bibliographic record
Abstract
This study aimed to determine the proportion of babies having retinopathy of prematurity (ROP) by a screening of high-risk babies and to determine the proportion of babies requiring laser for ROP. A tertiary hospital-based prospective descriptive study was carried out over a span of 2 years in 370 neonates admitted in the Department of Neonatology for prematurity, low birth weight (less than 1750 g), or neonates weighing 1750–2000 g and having an unstable clinical course. Neonates with congenital anomalies e.g., congenital cataract, neonates whose ROP screening was postponed beyond inclusion time due to unstable clinical course, and neonates whose parents refused to give consent were excluded from the study. Retinal evaluation by indirect ophthalmoscopy was done in all subjects till the maturation of the retina. The development of ROP and the need for laser therapy were considered as unfavourable outcomes. Risk factors were assessed using univariate and multivariate analysis. ROP was detected in 147 cases (39.73%). Stage 1 ROP was present in 38 cases (25.85%); stage 2, 3, 4 and 5 ROP was found in 26(17.69%), 78(53.06%), 3(2.04%) and 2(1.36%) cases, respectively. Gender had no correlation to ROP (p value 0.107). Birth weight was a significant factor in the occurrence of ROP. Maternal gestational age at the time of birth is a significant factor for the development of ROP (p value <0.001). Prevalence of ROP is increasing due to improved survival of preterm babies. Adherence to a ROP screening protocol in neonatal care facilities can prevent blindness in a large number of infants by early diagnosis and treatment.
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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 | 0.005 |
| 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 | 0.001 | 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".