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Record W4386895531 · doi:10.5693/djo.01.2023.05.001

ORIGINAL ARTICLES: Macular hole repair: effect of size and nonsupine posture on postoperative outcomes

2023· article· en· W4386895531 on OpenAlexaff
Efraim Berco, Raman Tuli, Nirojini Sivachandran, Nir Shoham-Hazon, Assaf Hilely

Bibliographic record

VenueDigital Journal of Ophthalmology · 2023
Typearticle
Languageen
FieldMedicine
TopicRetinal and Macular Surgery
Canadian institutionsDalhousie UniversityUniversity of Ottawa
Fundersnot available
KeywordsMacular holeMedicineVisual acuityOphthalmologySurgeryMedical recordCohortRetrospective cohort studyVitrectomyInternal medicine

Abstract

fetched live from OpenAlex

Background: Postoperative face-down positioning (FDP) for up to 2 weeks is believed to be necessary for successful closure of macular holes. FDP, however, can be disabling and uncomfortable and is a major burden for elderly patients. The aim of this study was to investigate how nonsupine posturing and macular hole size affect anatomical and functional success of macular hole closure. Methods: The medical records of patients with idiopathic macular holes who were treated surgically between 2016 and 2019 were reviewed retrospectively. Exclusion criteria included vitreomacular traction, previous retinal detachment, or chronic macular hole. Results: A total of 115 eyes of 115 patients were included. Average age was 69.2 ± 8.2 years; 63 patients (55%) were female. Anatomical success was achieved in 108 patients (94%) with a single operation. In small holes (<400 μm), closure was seen in 98% of cases (95% CI, 94%-100%); in large holes (≥400 μm), 90% of cases (95% CI, 76%-94%). Visual acuity remained stable or improved in 108 patients (92%). Average preoperative best-corrected visual acuity was 1.02 ± 0.45, with an overall improvement of 5 lines postoperatively. Small holes and large holes improved, with an average of 3 versus 7 lines gained, respectively. Conclusions: In this study cohort, favorable anatomical and functional outcomes were achieved without postoperative FDP. These outcomes are comparable to the traditional FDP approach.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.015
GPT teacher head0.306
Teacher spread0.292 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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