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Record W2087509751 · doi:10.1001/archopht.122.6.853

Photodynamic Therapy With Verteporfin for Subfoveal Choroidal Neovascularizationin Age-Related Macular Degeneration

2004· article· en· W2087509751 on OpenAlexaffabout
Sanjay Sharma

Bibliographic record

VenueArchives of Ophthalmology · 2004
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsHotel Dieu HospitalQueen's University
Fundersnot available
KeywordsVerteporfinChoroidal neovascularizationMacular degenerationMedicinePhotodynamic therapyVisual acuityConfidence intervalOphthalmologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the postapproval effectiveness of photodynamic therapy (PDT) with verteporfin for the treatment of predominantly classic subfoveal choroidal neovascularization (CNV) secondary to age-related macular degeneration. METHODS: Forty-five consecutive patients treated with PDT for subfoveal CNV were compared with an untreated historical control group. Control patients had subfoveal CNV and were first seen by us within 1 year before Health Canada's approval of verteporfin. Both groups were followed up for the development of significant visual loss, stability, or improvement. Multivariate models were constructed to evaluate the effectiveness of PDT, controlling for multiple covariates (age, sex, baseline visual acuity, follow-up time, lesion size, and number of treatments). RESULTS: Significant differences were noted in the change in visual acuity between those who did and did not receive PDT (chi(2) = 5.9, P =.048). Patients who received PDT were 2.9 times (95% confidence interval, 0.9-9.1) less likely to develop a moderate (>2 lines) visual loss (chi(2) = 3.2, P =.07). Controlling for covariates, patients who received PDT were 13.7 times (95% confidence interval, 1.4-132.6) more likely to develop a visual improvement of at least 1 line. CONCLUSION: Compared with historical controls, PDT was demonstrated to be effective for the treatment of predominantly classic subfoveal CNV.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score0.432

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.013
GPT teacher head0.275
Teacher spread0.261 · 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".

Quick stats

Citations11
Published2004
Admission routes2
Has abstractyes

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