ROP screening and treatment in four district-level special newborn care units in India: a cross-sectional study of screening and treatment rates
Bibliographic record
Abstract
Objective: Blindness from retinopathy of prematurity (ROP) in middle-income countries is generally due to absence of screening or inadequate screening. The objective of this study was to assess uptake of services in an ROP programme in four district-level special newborn care units in India. Design: Cross-sectional study. Setting: All four neonatal units of a state in India where model programme for ROP had been introduced. Patients: Infants eligible for screening and treatment of ROP between March and May 2017. Intervention: Data on sex, birth weight and gestational age of eligible infants were collected and medical records reviewed for follow-up. Main outcome measures: Proportion of eligible infants screened and for those screened, age at first screening, completion of screening, diagnosis and treatment received if indicated. The characteristics of infants screened and not screened were compared. Results: 137 (18%) of the 751 infants eligible for screening were screened at least once, with no statistically significant difference by sex. The mean birth weight and gestational age of those screened were significantly lower than those not screened. Among those screened, 43% underwent first screening later than recommended and 44% had incomplete follow-up. Fourteen infants (11% of those screened) were diagnosed with ROP. Five were advised laser treatment and all complied. Conclusion: Uptake, completion and timing of first screening was suboptimal. Some planned interventions including training of nursing staff, use of integrated data-management software and providing material for parent counselling, which have been initiated, need to be fully implemented to improve uptake of ROP screening services.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".