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Scleral-fixated and iris-fixated intraocular lens implantation or fixation:meta-analysis

2022· review· en· W4293372484 on OpenAlexaff
Tsz Hin Alexander Lau, Anubhav Garg, Marko M. Popovic, Peter J. Kertes, Rajeev H. Muni

Bibliographic record

VenueJournal of Cataract & Refractive Surgery · 2022
Typereview
Languageen
FieldMedicine
TopicIntraocular Surgery and Lenses
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineOphthalmologyIntraocular lensVisual acuityFixation (population genetics)Randomized controlled trialOptometrySurgeryPopulation

Abstract

fetched live from OpenAlex

Scleral fixation and iris fixation are common intraocular lens (IOL) implantation techniques performed because of zonulopathy. There is a lack of consensus regarding their comparative efficacy and safety. This study aims to compare the efficacy and safety outcomes after scleral-fixated (SF) vs iris-fixated (IF) IOL implantation or fixation in adults. A systematic literature search was conducted on Ovid MEDLINE, Embase, and Cochrane CENTRAL from 2005 to 2020. 785 eyes from 2 randomized controlled trials and 9 nonrandomized studies were included. There was no significant difference in the mean corrected distance visual acuity at the final follow-up ( P = .52) or absolute change in spherical equivalent ( P = .88) between SF IOL and IF IOL implantation. The incidence of vitreous hemorrhage was significantly higher in the SF IOL group (risk ratio = 3.66, 95% CI, 1.16-11.55, P = .03). There were no differences in visual acuity and refractive outcomes between SF IOL and IF IOL implantation or fixation. Trade-offs in complications exist between the 2 techniques.

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)0.008
metaresearch head score (Gemma)0.018
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: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0150.028
Bibliometrics0.0040.005
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.246
GPT teacher head0.391
Teacher spread0.144 · 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
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

Citations9
Published2022
Admission routes1
Has abstractyes

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