Photodynamic Therapy With Verteporfin for Subfoveal Choroidal Neovascularizationin Age-Related Macular Degeneration
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".