Comparison of different topical anesthetic methods for intravitreal injections: a randomized crossover study
Bibliographic record
Abstract
BACKGROUND: To evaluate the potential adjunctive effect of pledget anesthetic to topical proparacaine applied in a droplet form in patients undergoing intravitreal injections (IVI). METHOD: This is a single-centre, prospective, randomized, double-blinded crossover study. 60 patients were included. Patients receiving IVI were given topical 0.5% proparacaine drops then randomized in a 1:1 ratio to receive 0.5% proparacaine soaked pledget or normal saline soaked pledget as placebo. The patients would later be crossed over to receive the alternative intervention. Pain was assessed with a visual analog scale (VAS) and questionnaire immediately afterwards, 10-minutes and 20-minutes after injection. RESULT: Pain intensity as assessed on the visual analogue scale was lower for the placebo group compared to the pledget group immediately (2.51 cm vs. 2.8 cm), 10-minutes (1.81 cm vs. 2.13 cm) and 20-minutes (1.23 cm vs. 1.65 cm) after injection, however this was not statistically significant (p = 0.48, p = 0.43, p = 0.24 respectively). However, in a subgroup of treatment naïve patients, the addition of pledget anesthesia may lower pain and make IVI more tolerable. CONCLUSION: Additional pledget soaked with proparacaine does not enhance anesthesia compared to solely using topical proparacaine for IVI, except in a subset of treatment naïve patients.
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 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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".