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Record W2004804292 · doi:10.1159/000354548

Contemporary Management of Diabetic Retinopathy in Canada: From Guidelines to Algorithm Guidance

2013· article· en· W2004804292 on OpenAlexaffabout
Philip L. Hooper, Marie-Carole Boucher, Kevin Colleaux, Alan F. Cruess, Mark Greve, Wai‐Ching Lam, Stanley Shortt, Éric Tourville

Bibliographic record

VenueOphthalmologica · 2013
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsUniversity of VictoriaUniversity of British ColumbiaUniversity of TorontoDalhousie UniversityUniversité LavalUniversity of AlbertaUniversity of SaskatchewanUniversité de MontréalWestern University
Fundersnot available
KeywordsDiabetic retinopathyMedicineMacular edemaDiabetic macular edemaOptometryClinical PracticeDiabetes mellitusAlgorithmRetinaIntensive care medicineOphthalmologyFamily medicineComputer sciencePsychologyVisual acuityNeuroscience

Abstract

fetched live from OpenAlex

Recent advances in the therapeutic options and approaches for diabetic retinopathy (DR) and diabetic macular edema (DME) have resulted in improved visual outcomes for many patients with diabetes. Yet, they have also created many clinical dilemmas for treating ophthalmologists and retina specialists, including treatment selection, initiation, frequency and duration. With this in mind, a panel of Canadian retina specialists met and discussed the current clinical evidence as well as specific situations and scenarios commonly encountered in daily practice. They also shared their experiences and therapeutic approaches. This document, containing a consensus on treatment algorithms for various clinical scenarios, is the result of their lengthy and in-depth discussions and considerations. The intent is to provide a step-by-step approach to the treatment of DR and DME. Although clinicians are encouraged to use and refer to these algorithms as a guide for various situations, they are not meant to be a replacement for sound clinical judgment.

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 score0.669
Threshold uncertainty score0.865

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)0.0010.000

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 head0.048
GPT teacher head0.299
Teacher spread0.251 · 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

Citations15
Published2013
Admission routes2
Has abstractyes

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