Evidence for Changing Guidelines for Routine Screening for Retinopathy of Prematurity
Bibliographic record
Abstract
CONTEXT: Existing guidelines recommended by the Canadian Pediatric Society (CPS) and American Academy of Pediatrics (AAP) for routine screening for retinopathy of prematurity (ROP) remain controversial. OBJECTIVE: To determine whether current guidelines for routine screening for ROP should be changed. DESIGN: We examined data that were collected as part of a larger study of 14 neonatal intensive care units (NICUs) in Canada. We examined the effect of strategies using different birth weight (BW) and gestational age (GA) criteria for routine ROP screening, and performed a cost-effectiveness analysis. SETTING: The 14 NICUs (except one) are regional tertiary level referral centres serving geographic regions of Canada, and include approximately 60% of all tertiary-level NICU beds in Canada. PATIENTS: This large cohort included all 16 424 infants admitted to 14 Canadian NICUs from January 8, 1996, to October 31, 1997. INTERVENTIONS: None. MAIN OUTCOME MEASURE: Treatment for ROP. RESULTS: The most cost-effective strategy was to routinely screen only infants having a BW of 1200 g or less. This included all infants treated for ROP (except 1 outlier at 32 weeks GA and 1785 g BW), at a marginal cost per additional person with improved vision of $513 081 for screening patients between 28 weeks GA and 1200 g BW, compared with $1 800 039 and $2 075 874 for using the current AAP and CPS guidelines, respectively (cryotherapy outcomes). Results for laser therapy were similar, but costs were slightly lower. This strategy reduced the number of infants screened under the current CPS guidelines by 46%. CONCLUSION: Screening only infants having a BW of 1200 g or less is the most cost-effective strategy for routine ROP screening.
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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 | 0.058 | 0.320 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".