Reintervention rate in glaucoma filtering surgery: A systematic review and meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it