To ac tap or not to ac tap: Multi-centre outcomes of patients receiving anti-VEGF injections
Bibliographic record
Abstract
PurposeTo compare intraocular pressure (IOP) and retinal nerve fibre layer (RNFL) thickness in patients receiving intravitreal anti-vascular endothelial growth factor (VEGF) injections with and without anterior chamber paracentesis (ACP).MethodsThis multicentre retrospective cohort study included 269 injection-naïve eyes from 210 patients with neovascular age-related macular degeneration (AMD) or diabetic macular oedema (DME). A matched subset of 140 eyes (70 with ACP, 70 without) was selected based on age, sex, diagnosis, laterality, and number of injections. RNFL thickness (overall and by quadrant) was measured at baseline and one-year follow-up. Additional outcomes included IOP, visual acuity (VA), and central retinal thickness (CRT).ResultsThe matched cohort had a mean age of 71.06 ± 11.44 years, with 61.4% female participants. ACP eyes had worse baseline VA, higher IOP, and thicker CRT (p < 0.050, for all), but showed greater VA improvement (p = 0.023) and a trend towards greater CRT reduction (p = 0.061). RNFL thinning over one year did not differ between the groups (-3.24 ± 11.82 µm vs -2.95 ± 7.81 µm, p = 0.883). No major complications were observed.ConclusionACP did not significantly reduce RNFL thinning over one year but was well tolerated. It may be considered in patients at higher risk from transient IOP elevations. Future prospective studies are warranted to clarify its role in specific patient subgroups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".