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Record W2188911172 · doi:10.15353/cjo.71.667

Managing patients at risk for age-related macular degeneration: a Canadian strategy

2019· article· en· W2188911172 on OpenAlexfundvenueaboutno aff
Sohel Somani, Ann Hoskin-Mott, Adit Mishra, Brian H Book, Mark Chute, Ronald Gaucher, Barry Winter

Bibliographic record

VenueCanadian Journal of Optometry · 2019
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsnot available
FundersNovartis Pharmaceuticals Canada
KeywordsMacular degenerationGuidelineMedicineFamily medicineOptometryManagement strategyOphthalmologyPathology

Abstract

fetched live from OpenAlex

Background: To develop a consensus strategy for the management of patients at risk for age-related macular degeneration (AMD) for Canadian ophthalmologists, optometrists and physicians. Methods: Development of a consensus strategy began with a review of the literature and existing guidelines. A panel of retina specialists, ophthalmologists, and optometrists from across Canada assessed this evidence to distill what was learned and use this knowledge as the basis for developing a consensus strategy for managing patients at risk of AMD. Results: The expert panel has developed a series of recommendations for Canadian eyecare providers (eg. ophthalmologists, optometrists) and physicians to adopt as a preventive strategy for patients at risk of AMD. Interpretation: This consensus strategy is a practical guideline that can be adopted in the office setting to manage patients at risk of AMD and to advise patients with questions and concerns about AMD.

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)0.026
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.171
Threshold uncertainty score0.435

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.044
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.004
Science and technology studies0.0120.004
Scholarly communication0.0050.004
Open science0.0050.009
Research integrity0.0080.011
Insufficient payload (model declined to judge)0.0060.001

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 head0.009
GPT teacher head0.274
Teacher spread0.265 · 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 designNot applicable
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

Citations0
Published2019
Admission routes3
Has abstractyes

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