MétaCan
Menu
Back to cohort
Record W2580310361 · doi:10.17925/eor.2016.10.02.117

The Importance of Ocular, Systemic and Psychosocial Factors in the Management of the Diabetic Macular Oedema Patient with Antivascular Endothelial Growth Factor Therapy

2016· article· en· W2580310361 on OpenAlexaff
Richard Gale, Ângela Carneiro, Julie De Zaeytijd, P M Dodson, Sascha Fauser, João Figueira, Michael Larsen, Nicolas Leveziel, Michael A. Kapusta, José M. Ruiz‐Moreno, Enrico Peiretti, Christian Pruente, Reiner O Schlingemann, Christoph Scholda

Bibliographic record

VenueEuropean Ophthalmic Review · 2016
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsMcGill University
FundersNovo NordiskGlaxoSmithKlinePfizerAllerganEli Lilly and Company
KeywordsPsychosocialMedicineOphthalmologyOptical coherence tomographyPrimary careIntensive care medicineRetinalOptometryDiabetes mellitusFamily medicinePsychiatryEndocrinology

Abstract

fetched live from OpenAlex

Acomprehensive list of patient features that influence the management of patients with diabetic macular oedema (DMO) is discussed. These features are grouped into three overarching themes: ocular, systemic and psychosocial. Consensus statements about the relative importance of these features, supported by the literature, were formed by a panel of retinal experts. The major drivers influencing the management of DMO with anti-vascular endothelial growth factor (anti-VEGF) therapy are undoubtedly ocular specific, in particular visual acuity and optical coherence tomography (OCT) central retinal thickness. Systemic factors, such as control of glycated haemoglobin (HbA 1c ), blood pressure and serum lipid estimations, have limited direct influence on DMO management although they remain important considerations to communicate to the primary diabetic physician. A greater understanding is required on how many other factors, in particular psychosocial factors, influence the care of the DMO patient.

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 score0.031
Threshold uncertainty score0.244

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 head0.018
GPT teacher head0.264
Teacher spread0.246 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Ophthalmic ReviewSame topicRetinal Diseases and TreatmentsFrench-language works237,207