Reintervention rate in glaucoma filtering surgery: A systematic review and meta-analysis
Bibliographic record
Abstract
PURPOSE: Reintervention rate is an important factor impacting on patients, surgeons, and society. To date, only a few studies have focused on this topic. For this reason, a systematic review and meta-analysis was undertaken to assess the reintervention rate after glaucoma filtering surgery. MATERIALS AND METHODS: Prospective studies reporting the reintervention rate after glaucoma filtering surgery and with at least 12 months of follow-up were systematically searched on PubMed, Medline and Embase databases. The primary outcome was the total reintervention rate following surgery. Secondary outcomes were: the rate of manipulation, in-clinic and in-operating room reintervention; the reintervention rate for intraocular pressure (IOP) control and for complications; demographic, clinical and surgical variables associated with reintervention rate. RESULTS: Ninety-three studies with a total of 8345 eyes were eligible. The total reintervention rate was 1.84 (95% CI 1.57-2.13), with a lower rate for Baerveldt (0.53, 95% CI 0.29-0.83) and Preserflo (0.60, 95% CI 0.15-1.29), and a higher rate for Xen (4.26, 95% CI 2.59-6.31). The manipulation rate was 0.99 (95% CI 0.77-1.23), the in-clinic reintervention rate was 0.08 (95% CI 0.05-0.12) and the in-operating room reintervention rate was 0.28 (95% CI 0.22-0.35). The reintervention rate for IOP control was 1.26 (95% CI 1.04-1.51) and the reintervention rate for complications was 0.27 (95% CI 0.21-0.35). CONCLUSIONS: All types of surgery presented a total reintervention rate similar to the overall findings, except studies on Baerveldt and Preserflo Microshunt, with a lower rate, and Xen, with a higher rate. None of the variables evaluated were found to be directly associated with the explored outcomes.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.044 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".