Anterior chamber paracentesis for increased intraocular pressure with intravitreal injections: systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Intravitreal injections (IVIs) can cause a transient asymptomatic spike in intraocular pressure (IOP), which may cause irreversible damage to the patient's optic nerve. Anterior chamber paracentesis (ACP) is a well-established method to lower IOP. We aim to systematically review the literature, assessing the safety and efficacy of ACP for increased IOP with IVI of antivascular endothelial growth factor medications. METHODS: The following databases were used: MEDLINE, EMBASE, and CINAHL. Articles were included if they had human participants and discussed the use of ACP for increased IOP during IVIs. Key terms searched were anterior chamber paracentesis, intravitreal injections, and intraocular pressure. RESULTS: Our search captured 236 articles, and ultimately 13 studies were included in our review. Six studies were included in our meta-analysis of studies that reported the IOP after 30 minutes post-ACP. Ten included studies reported that ACP is a safe and effective procedure that lowers the IOP of patients during the IVI process. The overall pooled effect size is significant for IOP measurements 30 minutes after ACP is -1.54 with a 95% CI of -2.20 to -0.88 mm Hg, 5 minutes after ACP is -2.37 with a 95% CI of -2.77 to -1.97 mm Hg and 2 minutes after ACP is -5.09 with a 95% CI of -8.48 to -1.70 mm Hg. CONCLUSIONS: In conclusion, performing an ACP is a safe and effective way to reduce the transient spike in IOP after IVIs. However, this procedure is not without the potential for complications.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.040 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".