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Record W4383711227 · doi:10.1111/aos.15732

Antimicrobials and antiseptics: Lowering effect on ocular surface bacterial flora – A systematic review

2023· review· en· W4383711227 on OpenAlexaff
Mariantonia Ferrara, Francesca Gatti, David Lockington, Antonio Iaria, Stephen B. Kaye, Gianni Virgili, Pasquale Aragona, Francesco Semeraro, Vito Romano

Bibliographic record

VenueActa Ophthalmologica · 2023
Typereview
Languageen
FieldMedicine
TopicOcular Infections and Treatments
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsMedicinePerioperativeAntimicrobialFlora (microbiology)Intensive care medicineSystematic reviewCataract surgerySurgeryMEDLINEMicrobiologyBacteria

Abstract

fetched live from OpenAlex

Topical antimicrobials and antiseptics are used perioperatively to reduce the ocular surface bacteria flora (OSBF) that are involved in the development of post-operative infectious complications. However, their effectiveness is still a controversial topic. This systematic review, performed according to the PRISMA guidelines and registered in PROSPERO, aims to provide an overview of the efficacy of the agents currently used in peri-cataract surgery and -intravitreal injections (IVI) in lowering the OSBF. Although effective in lowering OSBF, perioperative topical antimicrobials are associated with the risk of resistance development, with no obvious additional benefit compared with topical antisepsis. Conversely, the effectiveness of topical antiseptics before cataract surgery and IVI is strongly supported. Based on the available evidence, perioperative antimicrobials are not recommended, whereas the perioperative use of antiseptics is strongly recommended as prophylactic treatment for lowering the infection due to OSBF. Post-operative antimicrobials may be considered in eyes at higher risk for infection.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.042
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.058
GPT teacher head0.362
Teacher spread0.305 · 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 teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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

Citations10
Published2023
Admission routes1
Has abstractyes

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