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Record W4415989248 · doi:10.1097/ms9.0000000000004145

Clinical presentation, diagnostic approaches, pathophysiology, and management of retinal pseudo-holes: a comprehensive synthesis of 35 peer-reviewed studies

2025· article· en· W4415989248 on OpenAlexaboutno aff
Mohammad Reza Famil Tokhmehchi

Bibliographic record

VenueAnnals of Medicine and Surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicRetinal and Macular Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsVitrectomyEpiretinal membranePars planaOptical coherence tomographyRetinalFundus photographyFluorescein angiographyFundus (uterus)

Abstract

fetched live from OpenAlex

Background: Retinal pseudo-hole (RPH) is a macular configuration defined on optical coherence tomography (OCT) by foveal depression without a full-thickness retinal defect, and may clinically resemble true/full-thickness macular hole (MH). Misclassification has therapeutic consequences. Objective: To synthesize evidence on the clinical presentation, diagnostic yield of imaging, pathophysiology, and management of RPH, with prespecified quantitative pooling where valid. Methods: A PRISMA-aligned systematic review identified 35 peer-reviewed studies (1990–2024). Two reviewers independently screened/extracted data (κ = 0.86) and appraised risk of bias (Cochrane RoB, Newcastle–Ottawa, adapted tools). Quantitative meta-analysis was performed only when outcomes, time-points, and variance were compatible. Results: Patients most commonly reported metamorphopsia, central scotoma, and blurred vision; many had preserved or mildly reduced BCVA. OCT consistently demonstrated the defining RPH signature and frequently revealed epiretinal membrane (ERM) and/or vitreomacular traction (VMT) that informed management; fluorescein angiography and fundus photography were adjunctive rather than discriminatory. Eleven of the 35 studies (11 studies) met pooling criteria for visual outcomes after pars plana vitrectomy (PPV) ± ERM peeling: the pooled mean BCVA improvement was + 2.1 Snellen lines (95% CI + 1.7 to + 2.5, p < 0.001; I 2 = 46%), and 70.3% (95% CI 63.9%–75.9%) achieved a ≥2-line gain (I 2 = 38%). Sensitivity analyses excluding high-risk studies produced similar effects (+2.0 to + 2.2 lines) with modestly reduced heterogeneity. Few studies reported extractable 2 × 2 data; therefore, pooled sensitivity/specificity for OCT was not estimated. Conclusions: Evidence supports an OCT-first, traction- and symptom-guided pathway: observation with serial OCT for minimally symptomatic RPH, and PPV ± ERM peeling for traction-positive, function-limiting presentations, with clinically meaningful average visual gains and low complication rates in experienced settings. Conclusions should be tempered by the heterogeneity and the fact that only 11 of 35 studies were quantitatively combinable; standardized outcomes and rigorous diagnostic-accuracy studies are priorities. Abstract Retinal pseudo-holes (RPH) are macular abnormalities that mimic accurate macular holes (MH) but lack full-thickness retinal disruption. This systematic review synthesizes findings from 35 peer-reviewed studies on the clinical presentation, diagnostic methods, pathophysiology, and management strategies for retinal pseudo-holes. The review highlights the challenges of diagnosing RPH due to its clinical similarity to MH, emphasizing the critical role of optical coherence tomography (OCT) in distinguishing between the two conditions. The pathophysiology of RPH is primarily attributed to vitreomacular traction (VMT) and epiretinal membranes (ERM), which exert mechanical forces on the macula, leading to a foveal depression. Management approaches range from conservative observation to surgical intervention, depending on the severity of symptoms. Surgical treatments, particularly pars plana vitrectomy with membrane peeling, have shown promising results in improving visual acuity, particularly in symptomatic patients. The review also discusses the need for further research into the long-term outcomes of RPH and the potential for newer imaging technologies to improve diagnostic accuracy and monitoring.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.449
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.246
GPT teacher head0.428
Teacher spread0.182 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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