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Laser Trabeculoplasty Perceptions and Practice Patterns of Canadian Ophthalmologists

2020· article· en· W3147553103 on OpenAlexaffabout
Elizabeth Y. Lee, Forough Farrokhyar, Enitan Sogbesan

Bibliographic record

VenueJOURNAL OF CURRENT GLAUCOMA PRACTICE · 2020
Typearticle
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsImpactMcMaster University
Fundersnot available
KeywordsGlaucomaMedicineGuidelineCross-sectional studyTrabecular meshworkOphthalmologyFamily medicineOptometry

Abstract

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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).

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.301
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.047
GPT teacher head0.337
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2020
Admission routes2
Has abstractyes

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