Prophylaxis Pharmacological Management for Patients with Intraoperative Floppy Iris Syndrome Risk Due to ?1-Adrenergic Receptor Antagonists Use
Bibliographic record
Abstract
Introduction: Intraoperative floppy iris syndrome (IFIS) remains a challenge that increases the risk of complications in patients undergoing cataract surgery who use ?1-adrenergic receptor antagonists. To date, no definite consensus on a preventive strategy for IFIS is available. The aim of this review is to assess various pharmacological managements to prevent IFIS in high-risk patients. Methods: This review was based on Preferred Reporting Items for Systematic Reviews and Meta- Analyses guidelines. A systematic search using PubMed, Science Direct, Cochrane Library, and WorldCat database was performed. Quality of each study was evaluated using Cochrane Risk of Bias 2.0 (RoB 2.0), Risk of Bias in Non-randomized Studies of Interventions (ROBINS-I), or Newcastle- Ottawa Scale (NOS). Results: The search identified 1589 articles of which 7 met the eligibility criteria. Experimental and observational studies between 2010 and 2018 were included. Pharmacological managements included in this review are administered in varying routes. Phenylephrine, lidocaine, a combination of lidocaine and epinephrine are given intracamerally. Other pharmacological managements included are sub-tenon injection of lidocaine, topical atropine, a combination of topical atropine with intracameral epinephrine, combined irrigation solution of phenylephrine and ketorolac, and mydriatic cocktail- soaked wick sponges. Conclusion: Various pharmacological managements for IFIS prophylaxis have shown promising potential. However, studies that evaluate the efficacy of each agent and comparison between these strategies are still limited. Further research is needed to determine the best prophylaxis strategy to reduce the incidence of IFIS.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".