Pain-Relieving Interventions for Retinopathy of Prematurity: A Meta-analysis
Bibliographic record
Abstract
CONTEXT: Retinopathy of prematurity eye examinations conducted in the neonatal intensive care. OBJECTIVE: To combine randomized trials of pain-relieving interventions for retinopathy of prematurity examinations using network meta-analysis. DATA SOURCES: Systematic review and network meta-analysis of Medline, Embase, Cochrane Central Register of Controlled Trials, Web of Science, and the World Health Organization International Clinical Trials Registry Platform. All databases were searched from inception to February 2017. STUDY SELECTION: Abstract and title screen and full-text screening were conducted independently by 2 reviewers. DATA EXTRACTION: Data were extracted by 2 reviewers and pooled with random effect models if the number of trials within a comparison was sufficient. The primary outcome was pain during the examination period; secondary outcomes were pain after the examination, physiologic response, and adverse events. RESULTS: Twenty-nine studies (N = 1487) were included. Topical anesthetic (TA) combined with sweet taste and an adjunct intervention (eg, nonnutritive sucking) had the highest probability of being the optimal treatment (mean difference [95% credible interval] versus TA alone = −3.67 [−5.86 to −1.47]; surface under the cumulative ranking curve = 0.86). Secondary outcomes were sparsely reported (2–4 studies, N = 90–248) but supported sweet-tasting solutions with or without adjunct interventions as optimal. LIMITATIONS: Limitations included moderate heterogeneity in pain assessment reactivity phase and severe heterogeneity in the regulation phase. CONCLUSIONS: Multisensory interventions including sweet taste is likely the optimal treatment for reducing pain resulting from eye examinations in preterm infants. No interventions were effective in absolute terms.
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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 | 0.020 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.050 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".