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Record W3033938270 · doi:10.4103/joco.joco_50_20

Canadian Opinions on Refractive Surgery and Approaches to Presbyopia Correction

2020· article· en· W3033938270 on OpenAlexaffabout
Helen Chung, Emi Sanders, Guillermo Rocha, Jamie Bhamra

Bibliographic record

VenueJournal of Current Ophthalmology · 2020
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsUniversity of ManitobaUniversity of CalgaryGimbel Eye Centre
Fundersnot available
KeywordsPresbyopiaMedicineOptometryRefractive surgeryOphthalmologyCornea

Abstract

fetched live from OpenAlex

PURPOSE: To explore the opinions of Canadian ophthalmologists on refractive and presbyopia-correcting surgeries. METHODS: We distributed an online survey to the Canadian Ophthalmological Society members, covering laser refractive surgery (LRS), femtosecond laser-assisted cataract surgery (FLACS), lenticular refractive surgery (lenRS) that includes cataract refractive surgery (CRS) with premium intraocular lens (IOL) implantation, and presbyopia correction. RESULTS: There were 68 (7.6%) total respondents. Most respondents would not consider LRS (62.5%) nor FLACS (73.9%) for themselves. Male sex and performance of LRS or FLACS was significantly associated with consideration of these procedures for self. Most respondents (59.3%) would consider lenRS for themselves. The top method of personal presbyopia correction was spectacles, chosen by 52.5%. CONCLUSIONS: When surveying the wide body of Canadian ophthalmologists, most respondents preferred spectacle correction of presbyopia and would consider lenRS, but not LRS or FLACS for themselves. Surgeons performing these procedures were more likely to consider them for self.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.068
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.000

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 head0.311
GPT teacher head0.394
Teacher spread0.083 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2020
Admission routes2
Has abstractyes

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