Subconjunctival Bevacizumab Injection in Glaucoma Filtering Surgery: A Case Control Series
Bibliographic record
Abstract
Aims. To describe the use of subconjunctival bevacizumab (SCB) injection in the combined cataract and glaucoma filtering surgery (GFS). Methods. Retrospective comparative case series. Thirty eyes of twenty-eight patients who had GFS followed by SCB injection as part of post-operative management were included (Group SCB). The types of GFS included trabeculectomy and non-penetrating glaucoma surgery (NPGS) with mitomycin-C. Outcome measures included the reduction of intraocular pressure (IOP) and medications. Age-matched patients who had the same types of surgery without SCB were selected as a control group (Group C). Results. The types of GFS were: combined cataract surgery and NPGS (SCB: 20; C: 24), phacotrabeculectomy (SCB: 6; C: 3), NPGS (SCB: 3; C: 2) and trabeculectomy alone (SCB: 1; C: 1). The average follow-up time was 16.9 (±8.2) months in the SCB group and 19.6 (±11.5) months in the controls. 1.25 mg of bevacizumab was injected on average 14.1 (range: 3-42) days post-GFS. The mean IOP decreased from 21.9 (±9.8) to 11.9 (±4.7) mmHg in the controls and from 19.6 (±8.9) to 14.0 (±4.7) mmHg in the SCB group. There was no statistically significant difference between the two groups (P = 0.11). Complications included three cases of branch vein occlusion in the SCB group. Conclusions. SCB did not result in better outcome in term of IOP reduction. Clinicians should monitor its side effects in glaucoma patients.
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 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".