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Record W6977707893 · doi:10.6084/m9.figshare.29500407

Intracameral antibiotics for endophthalmitis prophylaxis in cataract surgery: a meta-analysis

2025· dataset· en· W6977707893 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2025
Typedataset
Languageen
FieldMedicine
TopicOcular Infections and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAntibioticsPhacoemulsificationEndophthalmitisCataract surgeryIncidence (geometry)Antiseptic

Abstract

fetched live from OpenAlex

Despite numerous studies, there is no consensus on the use of intracameral (IC) antibiotics to prevent postoperative endophthalmitis (POE) after cataract surgery. A systematic search of Ovid MEDLINE, EMBASE, and Cochrane CENTRAL (inception–April 2021; PROSPERO ID: CRD42021248702) identified studies reporting POE risk in eyes treated with or without IC antibiotics. Study quality was assessed using the Cochrane risk-of-bias tool and Newcastle-Ottawa Scale. Meta-analysis was conducted using a random-effects model. Among 23 studies (2 RCTs, 21 observational; 5,442,686 eyes), POE incidence was lower with IC antibiotic use (0.038% vs. 0.096%, RR: 0.15, CI: [0.10, 0.23], ARR: 0.058, p < 0.001). In phacoemulsification cases, POE was 0.043% with IC antibiotics vs. 0.14% without (RR: 0.11, CI: [0.06, 0.20], ARR: 0.097, p < 0.001). POE risk was lower with combined IC and topical antibiotics (0.02% vs. 0.056%, RR: 0.40, p < 0.001). Gram-positive bacteria were more common in POE cases without IC antibiotics; gram-negatives were more frequent with IC use. IC antibiotics reduced POE risk, though evidence quality was low to very low due to potential confounding. More rigorous studies accounting for surgical technique, antiseptic use, and adjunctive antibiotics are needed.

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 imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.019
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0190.064
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.104
GPT teacher head0.357
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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