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Record W4409591832 · doi:10.1016/j.jcjo.2025.03.012

Surgical outcomes of small and medium macular holes with or without use of internal limiting membrane flaps

2025· article· en· W4409591832 on OpenAlexaffvenueabout
Carolin Aizouki, Graeme K. Loh, Matthew Tennant, Parampal S. Grewal, Mark E. Seamone

Bibliographic record

VenueCanadian Journal of Ophthalmology · 2025
Typearticle
Languageen
FieldMedicine
TopicRetinal and Macular Surgery
Canadian institutionsDalhousie UniversityUniversity of Alberta
Fundersnot available
KeywordsMacular holeInternal limiting membraneMedicineVisual acuitySignificant differenceLimitingSurgeryOphthalmologyVitrectomy

Abstract

fetched live from OpenAlex

OBJECTIVE: This study aims to evaluate the long-term visual and anatomical outcomes of the inverted flap technique in small-to-medium sized macular holes (MH). DESIGN: Retrospective chart review. PARTICIPANTS: Consecutive patients who underwent macular hole surgery at a single retina center in Alberta, Canada. METHODS: Macular hole surgeries were stratified on the basis of size. Postsurgical visual outcomes and hole closure rates were captured. Large holes were excluded from analysis. RESULTS: A total of 239 medium and 252 small holes were included; 65 medium and 40 small holes had internal limited membrane (ILM) flaps. In the small MH group, mean hole size was 170.7 μm, and visual gain averaged 6.4 ETDRS letters, with no significant difference based on surgical technique (p = 0.7). All small holes closed with ILM flapping with 4 (2%) of the ILM peels not closing, this was not significant (p = 0.4). In medium MH, mean hole size was 320.9 μm, and visual gain averaged 11.9 ETDRS letters, with no significant difference based on surgical technique (p = 0.5). All medium holes closed with ILM flapping, with 8 (5%) of the standard ILM peel not closing; this was not significant (p = 0.08). CONCLUSIONS: There was no significant difference in either surgical closure rates or visual acuity gains when comparing ILM peeling to the flap technique for both small and medium-sized MH.

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)0.000
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 score0.323
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.040
GPT teacher head0.288
Teacher spread0.248 · 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
Published2025
Admission routes3
Has abstractyes

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