Laser Trabeculoplasty Perceptions and Practice Patterns of Canadian Ophthalmologists
Bibliographic record
Abstract
Aim: To describe the current practice patterns and perceptions of Canadian ophthalmologists using laser trabeculoplasty (LTP).Materials and methods: A cross-sectional survey of 124 members of the Canadian Ophthalmological Society (COS) who perform LTP was conducted.Descriptive statistics and Chi-square comparative analyses were performed on anonymous self-reported survey data.Results: Of the 124 respondents, 34 (27.4%) completed a glaucoma fellowship.Use of selective laser trabeculoplasty (SLT) (94.4%) was preferred over argon laser trabeculoplasty (ALT) (5.6%).The most frequently cited reasons for SLT preference was less damage to trabecular meshwork (30.7%), availability (16.2%), and repeatability (16.2%).In all, 47.6% of the respondents performed LTP concurrently with medical treatment, 33.9% used it after medical treatment, and 17.7% used it as first-line treatment.Majority (87.1%) of the respondents believed that SLT is effective when repeated.In suitable patients, 41.9% of the respondents stated on average they repeat SLT once, 26.6% twice, and 19.4% greater than 2 times, respectively.Of those who repeat SLT on patients, 80.7% found repeat SLT treatments have good outcomes for patients.In all, 105 (84.7%) ophthalmologists responded they would benefit from an LTP practice guideline.Significantly more ophthalmologists without glaucoma fellowships perceived they would benefit from a practice guideline (p value <0.001). Conclusion:This survey provides valuable practical information on how LTP is used in the treatment of glaucoma in Canada. Clinical significance:The findings may serve as a baseline survey to trend future practices.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".