Reducing rates of endophthalmitis from intravitreal injections – strategies and areas of controversy
Bibliographic record
Abstract
PURPOSE OF REVIEW: Post-injection endophthalmitis (PIE) is the most concerning complication that accompanies intravitreal injections. This review discusses the recent literature in endophthalmitis prophylaxis including types of antisepsis, the use of topical antibiotics, methods of anesthesia, masking, and office-based versus operating room-based injections. RECENT FINDINGS: Povidone iodine (PI) remains the gold standard for PIE prophylaxis. Chlorhexidine gluconate (CHG) is an alternative antiseptic agent utilized in other areas of medicine with similar broad spectrum antibacterial activity. Recent clinical trials have demonstrated that the rate of endophthalmitis is similar with CHG prophylaxis compared to PI prophylaxis while offering improved patient comfort at a similar cost. Routine use of topical antibiotics should be avoided as they do not appear to reduce endophthalmitis risk and may promote bacterial resistance. All methods of anesthesia appear to be acceptable. In-office injections are not associated with an increased rate of endophthalmitis compared to operating room injections. SUMMARY: The rate of post-injection endophthalmitis is extremely low due to a myriad of measures employed by retina specialists. Topical antisepsis is the most important tool to combat post-injection endophthalmitis. CHG is emerging as an alternative to PI due to its efficacy and enhanced patient comfort.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".