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Record W2909838604 · doi:10.1371/journal.pone.0210205

Effect of anterior capsule polish on visual function: A meta-analysis

2019· review· en· W2909838604 on OpenAlexaboutno aff
Mengyao Han, Ai-Hua Yu, Jing Yuan, Xiaojun Cai, Jiangbo Ren

Bibliographic record

VenuePLoS ONE · 2019
Typereview
Languageen
FieldMedicine
TopicIntraocular Surgery and Lenses
Canadian institutionsnot available
FundersWuhan University
KeywordsJadad scaleCapsuleMedicineMeta-analysisOphthalmologyOdds ratioVisual acuityRandomized controlled trialCohort studySurgeryInternal medicineBiology

Abstract

fetched live from OpenAlex

PURPOSE: To investigate the relationship between anterior capsule polish and visual function. METHODS: Data were obtained from Pubmed, Embase, Web of Science, WanFang, VIP and CNKI up to the end of May 2018, without any date or language restrictions for trials. The modified Jadad scale and the newcastle-ottawa scale were used to assess the quality of included studies. Uncorrected visual acuity (UCVA) and posterior capsule opacification (PCO) were used as outcome variables. Data on anterior capsule polish were pooled using weighted, random-effect meta-analysis. RESULTS: One randomized controlled trial and 4 observational cohort studies involving 2533 patients were included in the analyses. There was a statistically significant difference of UCVA (OR 1.92, 95% CI 1.41-2.61) between the polish group and the control group, indicating that anterior capsule polish improved UCVA. Further studies with continuous data also suggested that anterior capsule polish was associated with good UCVA (MD 0.11, 95% CI 0.06-0.16). Posterior capsule opacification rate for 1-year or longer follow-up were extracted for 2561 eyes in 3 studies. Posterior capsule opacification rate was lower in the anterior capsule polish group according to summary odds ratio on PCO rate (OR 0.42 95% CI 0.24-0.73). CONCLUSIONS: Anterior capsule polish prevents complication of modern cataract surgery and benefits on visual function in short term follow-up period.

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)0.000
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: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.087
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0090.006
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.194
GPT teacher head0.362
Teacher spread0.168 · 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 designMeta-analysis
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

Citations11
Published2019
Admission routes1
Has abstractyes

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