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Record W3123826157 · doi:10.1097/ico.0000000000002639

Surgeon Preferences for Endothelial Keratoplasty in Canada

2021· article· en· W3123826157 on OpenAlexaffabout
Eli Kisilevsky, Divya Srikumaran, Hall F. Chew

Bibliographic record

VenueCornea · 2021
Typearticle
Languageen
FieldMedicine
TopicCorneal surgery and disorders
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOphthalmologyMedicineOptometry

Abstract

fetched live from OpenAlex

PURPOSE: To quantify practice patterns and assess attitudes and barriers to performing Descemet membrane endothelial keratoplasty (DMEK) in Canada. METHODS: An anonymous online survey was distributed to all corneal surgeons included in the Canadian Ophthalmological Society's database. RESULTS: Of 70 listed surgeons, 41 responses were collected (58.6% response rate). Most respondents were practicing in university hospitals (43.9%) or private practice (43.9%) and were involved in residency teaching (77.5%). Most respondents performed DMEK surgery (78%), and most surgeons prepared their own DMEK grafts (62%). Surgeons who were in practice for more than 25 years were less likely to perform DMEK (75% vs. 13%, P = 0.009) and performed fewer corneal transplantation in the previous year (mean 28 vs. 44, P = 0.022). Those who were not performing DMEK reported access to preprepared tissue (77.8%), access to wet laboratory courses (50%), and assistance or mentorship (50%) as common facilitators to start performing DMEK surgery. CONCLUSIONS: DMEK is the preferred surgery for endothelial disease among Canadian corneal surgeons. Eye banks play a key role in increased adoption by ensuring an adequate supply of tissue and prestripping tissue for surgeons new to DMEK to be confident in performing it. Ensuring adequate supply of donor tissue and supplementary surgeon training can ensure that DMEK surgery is widely available in Canada.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.627
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.024
GPT teacher head0.237
Teacher spread0.213 · 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

Citations5
Published2021
Admission routes2
Has abstractyes

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