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Longterm follow‐up of patients with multifocal choroiditis and panuveitis

2004· article· en· W1275954026 on OpenAlexaffabout
Raul N. G. Vianna, Pınar Özdal, J.P. Souza Filho, Jean Deschênes

Bibliographic record

VenueActa Ophthalmologica Scandinavica · 2004
Typearticle
Languageen
FieldMedicine
TopicOcular Diseases and Behçet’s Syndrome
Canadian institutionsMcGill UniversityRoyal Victoria HospitalMcGill University Health CentreRoyal Victoria Regional Health Centre
Fundersnot available
KeywordsChoroiditisTerm (time)MedicineOphthalmologyOptometryPhysicsAstronomy

Abstract

fetched live from OpenAlex

PURPOSE: To report the visual prognosis and longterm complications in patients with multifocal choroiditis and panuveitis (MCP). METHODS: A retrospective study was performed with patients who met inclusion criteria for MCP at the Uveitis Clinic, Royal Victoria Hospital, McGill University, Montreal, Canada. Information collected included duration of follow-up, visual acuity (VA) measured at each clinical visit, ocular and systemic treatment and ocular complications observed during follow-up. RESULTS: Nineteen patients (37 eyes) with MCP with a mean follow-up of 76.9 months were studied. Kaplan-Meier survival analysis showed a decrease in the proportion of patients with a final VA > or = 20/40 over time. Cystoid macular oedema was seen in 29.7% of the eyes and was the most frequent macular abnormality observed in our group. On the other hand, choroidal neovascularization was detected in only six (16.2%) of the eyes, but was related to VA < 20/200 in four of these eyes. Glaucoma was detected in 10.8% of the eyes. Cataract (posterior subcapsular and/or nuclear) was the most common longterm complication, occurring in 40% of affected eyes. Cataract surgery improved the VA in 83.3% of these eyes. CONCLUSION: The visual acuity of patients with MCP decreases with time. Visual loss can occur from complications following the inflammation itself and/or iatrogenic induced by the chronic use of corticosteroids.

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 score0.008
Threshold uncertainty score0.719

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 head0.014
GPT teacher head0.251
Teacher spread0.238 · 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
Published2004
Admission routes2
Has abstractyes

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