Lid splinting eyelid retraction technique: a minimised sterile approach for intravitreal injections
Bibliographic record
Abstract
BACKGROUND/AIMS: To describe an alternative technique for avoiding contact with the lids and lashes, without the use of a lid speculum, during intravitreal anti-vascular endothelial growth factor injections. METHODS: Retrospective case series of all patients undergoing intravitreal injections of bevacizumab and ranibizumab, with the lid splinting retraction technique from January 2010 to December 2015. Injections performed by six vitreoretinal specialists were included. The key preinjection ocular surface preparation includes topical anaesthetic, 5% povidone-iodine and a subconjunctival injection of 2% lidocaine with epinephrine. A second instillation of 5% povidone-iodine is given and the intravitreal injection is then performed. No lid speculum is used. A search of the electronic medical records identified patients diagnosed with postinjection endophthalmitis and charts were reviewed to ensure inclusion criteria were met. The main outcome measure was incidence of postinjection endophthalmitis. RESULTS: A total of 78 009 consecutive intravitreal injections were performed, of which 22 207 were bevacizumab and 55 802 were ranibizumab. In this cohort of patients (n=6320), 12 cases of endophthalmitis developed, corresponding to a rate of 0.015%. CONCLUSIONS: The technique of eyelid retraction for intravitreal injection has a low rate of endophthalmitis, similar to the reported rates using a metal lid speculum. This is beneficial for both the physician and the patient as it minimises patient discomfort as well as the duration of the procedure. To our knowledge, this is one of the largest studies performed to date evaluating intravitreal injection-related endophthalmitis.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Case report | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".