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Record W4391784100 · doi:10.35749/j91km823

Diabetes, Diabetic Retinopathy, and Intravitreal Injection as a Risk Factor of Posterior Capsular Rupture in Phacoemulsification Cataract Surgery :A Systematic Review and Meta Analysis

2024· article· en· W4391784100 on OpenAlexaboutno aff
Ni Luh Putu Nurindah Sukmawati, I Gusti Putu Eka Suryawan Widnyana

Bibliographic record

VenueOphthalmologica Indonesiana · 2024
Typearticle
Languageen
FieldMedicine
TopicIntraocular Surgery and Lenses
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePhacoemulsificationOphthalmologyDiabetic retinopathyDiabetes mellitusCataract surgeryRisk factorMeta-analysisSurgeryInternal medicineVisual acuityEndocrinology

Abstract

fetched live from OpenAlex

Abstract
 Introduction & Objectives : The risk of PCR in patients with diabetes and its sequential complication including diabetic retinopathy and Intravitreal Injection (IVI) was still under debate. This study was aimed to summarize the risk of diabetes, diabetic retinopathy, and intravitreal injection towards PCR events during phacoemulsification cataract surgery.
 Methods : A systematic literature search was performed up to March 2023 from the last 10 years publications using Medline and Cochrane library databases. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) of diabetes, diabetic retinopathy, and IVI with PCR events were calculated using random-effects models. The quality of included studies was appraised using the Newcastle-Ottawa Scale.
 Results : Six studies about diabetes, six studies about diabetic retinopathy and seven studies about IVI were included in this meta-analysis. The pooled odds ratios of PCR in eyes with diabetes was 1.22 (95% CI: 1.16-1.28) I2 =0.0%, with the diabetic retinopathy patients was 1.23 (95% CI: 1.13-1.33) I2 =64.3%, and with prior IVI was 1.05 (95% CI: 1.03-1.08) I2 =51%.
 Conclusion : This meta-analysis emphasize that diabetes, diabetic retinopathy, and previous IVI significantly increase the risk of PCR on future phacoemulsification surgery. Further studies are needed to elucidate the underlying mechanism.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.275
Teacher spread0.253 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2024
Admission routes1
Has abstractyes

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