MétaCan
Menu
Back to cohort

MASSIVE PERIPAPILLARY SUBRETINAL NEOVASCULARIZATION

2004· article· en· W2046702614 on OpenAlexaff
Peter J. Kertes

Bibliographic record

VenueRetina · 2004
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsLoginComputer scienceWorld Wide WebRegister (sociolinguistics)Logo (programming language)Internet privacyComputer security

Abstract

fetched live from OpenAlex

PURPOSE: To describe the course and outcome of three consecutive patients with massive peripapillary subretinal neovascularization secondary to ocular histoplasmosis syndrome managed with submacular surgery. METHODS: Three eyes of three consecutive patients with progressive and massive peripapillary subretinal neovascularization secondary to ocular histoplasmosis syndrome were treated with submacular surgery and followed up for a mean of 41 months (range, 11-59 months). The main outcomes were surgical complications, visual acuity, and subretinal membrane recurrence. RESULTS: Visual acuity improved in each patient from counting fingers, 20/25, and 20/400 preoperatively to 20/50, 20/20, and 20/20, respectively, at the last follow-up visit. With respect to complications, Patient 3 was found to have an operculated retinal tear approximately 1 month postoperatively, which was successfully treated with argon laser retinopexy. There were no cases of visually significant cataract, rhegmatogenous retinal detachment, or recurrent choroidal neovascularization in any of the operated eyes during the period of follow-up. CONCLUSIONS: Submacular surgery proved safe and beneficial in this small series of young patients with massive peripapillary subretinal neovascularization secondary to ocular histoplasmosis syndrome and should be considered in this relatively uncommon clinical presentation.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.009
GPT teacher head0.255
Teacher spread0.247 · 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 designCase report
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

Citations11
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueRetinaSame topicRetinal Diseases and TreatmentsFrench-language works237,207