Recurrence of choroidal neovascularisation after photodynamic therapy in patients with age-related macular degeneration
Bibliographic record
Abstract
AIM: To determine the incidence of recurrence of choroidal neovascularisation (CNV) 18 months after cessation of photodynamic therapy (PDT) with verteporfin monotherapy in patients with age-related macular degeneration (AMD). METHODS: This was a prospective interventional cohort study. The sample consisted of 108 individuals with CNV secondary to AMD which was treated with PDT. Data on demographics, pre-PDT and post-PDT Early Treatment of Diabetic Retinopathy Study (ETDRS) acuity, pre-PDT lesion size and composition were collected for each participant. All participants returned for fundus photographs and ETDRS acuity measurements 18 months after their final PDT, which were compared with the same measurements from the final treatment session to determine recurrence status. Recurrences were classified primarily on the basis of haemorrhage and increased lesion size. RESULTS: Recurrences were observed in 36 of 108 (33%) eyes. 23 of 36 (64%) recurrences were clinically meaningful. Of the explanatory variables considered, only final PDT acuity was significantly different between those that recurred (45.5 ETDRS letters) and those that did not (38.4 letters; p = 0.03). CONCLUSION: CNV recurrences are common after PDT for AMD, occurring in 33% of eyes in this study. Visual acuity measured at the final PDT treatment visit may be a predictor of subsequent recurrence.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".