A Meta‐analysis of the Efficacy of Ocular Prophylactic Agents Used for the Prevention of Gonococcal and Chlamydial Ophthalmia Neonatorum
Bibliographic record
Abstract
INTRODUCTION: Neonatal eye prophylaxis has been routine in North America for more than a century. Contextual changes justify reexamining this practice, and prompted a systematic review of the efficacy of prophylactic agents. METHODS: We searched MEDLINE (1966-2008), EMBASE (1980-2008), CINAHL (1982-2008), and the Cochrane library (the first quarter of 2008) for relevant clinical trials and hand-searched the resulting reference lists. We independently evaluated eligibility and study quality. Meta-analyses were performed using a random effects model. RESULTS: Each of the eight included studies had substantial methodologic weaknesses. Data to estimate the efficacy of prophylaxis in the prevention of gonococcal ophthalmia neonatorum (GON) were not available. One study found no differences in rates of chlamydial ophthalmia neonatorum (CON) when three agents were compared to no prophylaxis: silver nitrate (relative risk [RR] = 1.06; 95% confidence interval [CI], 0.55-2.02; 2225 newborns), erythromycin (RR = 0.93; 95% CI, 0.48-1.79; 2306 newborns), and tetracycline (RR = 0.82; 95% CI, 0.42-1.63; 2299 newborns). No statistically significant differences were found between agents in the prevention of GON. Erythromycin and povidone-iodine both decrease the risk of CON when compared to silver nitrate (RR = 0.71; 95% CI, 0.52-0.97; 4514 newborns, and RR = 0.52; 95% CI, 0.38-0.71; 2005 newborns, respectively). DISCUSSION: Failure rates of universal eye prophylaxis support reexamination of this policy where the prevalence of maternal infection is low.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.044 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.059 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".