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Record W4224325082 · doi:10.1155/2022/1553719

Prognostic Factors for Visual Outcomes in Closed Idiopathic Macular Holes after Vitrectomy: Outcomes at 4 Years in a Monocentric Study

2022· article· en· W4224325082 on OpenAlexafffundabout
Alexandre Lachance, Mélanie Hébert, Eunice You, Jean-Philippe Rozon, Serge Bourgault, Mathieu Caissie, Éric Tourville, Ali Dirani

Bibliographic record

VenueJournal of Ophthalmology · 2022
Typearticle
Languageen
FieldMedicine
TopicRetinal and Macular Surgery
Canadian institutionsUniversité LavalHôpital du Saint-Sacrement
FundersCanadian Institutes of Health Research
KeywordsMedicineVitrectomyOphthalmologyOptometryVisual acuity

Abstract

fetched live from OpenAlex

Purpose. To identify predictive factors of visual outcomes in the eyes after successful macular hole (MH) surgery. Methods. It is a retrospective monocentric study of the eyes that underwent successful vitrectomy for full-thickness MH in an academic, tertiary care center (CHU de Québec–Université Laval, Québec, Canada) between 2014 and 2018. We included a single eye per patient and excluded the eyes with ocular comorbidities. Clinical and anatomical features of patients were collected, including demographics, MH duration, baseline MH size, baseline visual acuity (VA), and final VA. Multiple logistic regressions were performed to determine predictive factors of VA ≥70 ETDRS letters (Snellen equivalent: 20/40) and VA gain ≥15 ETDRS letters at final follow-up. Areas under the receiver operating characteristic curve (AUC) were used to determine the performance of each model and identify the Youden index maximizing performance at a given threshold. Results. A total of 460 eyes were included in this study; 274/460 eyes (60%) achieved final VA ≥70 ETDRS letters and 304/460 eyes (66%) had a VA gain ≥15 ETDRS letters at 24 months follow-up. Multiple logistic regression analyses showed that the main predictive factors for final VA ≥70 ETDRS letters (model AUC = 0.716) were baseline VA (OR = 1.064; p < 0.001 ), MH duration (OR = 0.950; p = 0.005 ), and age (OR = 0.954; p = 0.004 ). Predictors of VA gain ≥15 ETDRS letters at final follow-up (model AUC = 0.615) were baseline VA (OR = 0.878; p < 0.001 ), MH duration (OR = 0.940; p < 0.001 ), and MH size (OR = 0.998; p = 0.036 ). Thresholds for the final VA ≥70 ETDRS letters model and the VA gain ≥15 ETDRS letters model were VA ≥55.5 ETDRS letters (Snellen equivalent: 6/30) and MH size of 237 μm, respectively. Conclusion. The eyes with shorter MH duration, smaller MH size, and higher preoperative VA achieved better visual outcomes after successful MH surgery.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.049
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.331
Teacher spread0.303 · 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 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

Citations5
Published2022
Admission routes3
Has abstractyes

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