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Record W4406215151 · doi:10.1186/s40942-024-00623-8

Suprachoroidal injection of triamcinolone acetonide as adjuvant to surgical treatment of epiretinal membrane

2025· article· en· W4406215151 on OpenAlexaff
Francesco Morescalchi, Federico Gandolfo, Vito Romano, Andrea Baldi, Francesco Semeraro

Bibliographic record

VenueInternational Journal of Retina and Vitreous · 2025
Typearticle
Languageen
FieldMedicine
TopicRetinal and Macular Surgery
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsMedicineVitrectomyEpiretinal membraneTriamcinolone acetonideOphthalmologyAcetonideMacular edemaVisual acuitySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: To analyse the effect of suprachoroidal injection (SChI) of triamcinolone acetonide (TA) on macular thickness (CRT), ectopic inner foveal layer thickness (EIFL-T) and best corrected visual acuity (BCVA) in pseudophakic patients undergoing vitrectomy for epiretinal membrane (iERM) compared to intravitreal injection of TA (IVTA). METHODS: Prospective matched comparison of patients undergoing vitrectomy for Govetto stage 3 and 4 iERM. 25 eyes receiving IVTA (G-1) were compared to 23 eyes receiving SChI-TA (G-2) during vitrectomy. Primary outcome was change in BCVA, CRT, EIFL-T before surgery and 1, 3 and 6 months after surgery. Secondary outcome was the incidence of cystoid macular edema (CME). RESULTS: Six months after surgery, G2 had a greater mean reduction in CRT (-222 µm vs -131 µm) and EIFL-T (-200 µm vs -104 µm) than G1. BCVA improved more in G2 than in G1 (p = 0.02). Foveal depression reformed in 43% of cases in G-2 and 16% of cases in G-1. Incidence of postoperative CME was 16% in G-1 and 4.3% in G-2. CONCLUSIONS: During vitrectomy for iERM, SChI-TA was more effective than IVTA in reducing CRT and EIFL-T and improving BCVA. SChI-TA was effective in preventing postoperative CME. SChI-TA treatment was safe and reproducible and did not affect postoperative IOP. Trial registration NP6289-June 18th, 2024 (retrospectively registered).

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score0.386

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 head0.011
GPT teacher head0.330
Teacher spread0.319 · 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 designBench or experimental
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 routes1
Has abstractyes

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