Association between sepsis and retinopathy of prematurity: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective To explore the association between sepsis and retinopathy of prematurity (ROP) in premature infants. Design A systematic review and meta-analysis. Data sources We performed a systematic search of PubMed, the Cochrane Library and Embase from 1 January, 2000, to 1 January, 2018, with no language restrictions and reported the relationship between sepsis and ROP. Eligibility criteria Original observational studies, including cohort studies and case-control studies. Data extraction and synthesis Two reviewers independently completed the study selection and data extraction. The OR and corresponding 95% CI were used to measure the risk of sepsis in patients with ROP. The heterogeneity between studies was evaluated using Cochran’s Q test and the I 2 statistic. The Newcastle-Ottawa Scale was adopted to evaluate the quality of each of the included studies, and the Grading of Recommendations Assessment, Development and Evaluation approach was used to assess the quality of the evidence. Results Sixteen studies with a total sample size of 12 466 premature infants and 2494 cases of ROP were included in this meta-analysis. Adjusted analysis showed that sepsis was closely related to any stage of ROP (OR = 1.57, 95% CI 1.31 to 1.89) and severe stage of ROP (OR = 2.33, 95% CI 1.21 to 4.51) in premature infants, with 56.3% and 81.8% heterogeneity, respectively. Subgroup analyses showed that heterogeneity was obvious in prospective cohort studies (I 2 = 62.1%, p < 0.001). In a sensitivity analysis, we found that removing any single study did not significantly change the overall effect value. The quality of the evidence was rated as low for both any stage of ROP and severe stage of ROP. Conclusions Sepsis increases the risk of ROP in preterm infants. However, considering that all included studies are observational and causality can rarely be established, additional evidence is needed to substantiate this finding and provide advice for practice.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.029 | 0.046 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".