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Record W4383683653 · doi:10.58931/cect.2023.2227

Anti-VEGF therapy in pregnancy and breastfeeding

2023· article· en· W4383683653 on OpenAlexaff
Amy Basilious, Rajeev H. Muni, Verena R. Juncal

Bibliographic record

VenueCanadian Eye Care Today · 2023
Typearticle
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsSt Joseph's Health CareWestern University
Fundersnot available
KeywordsMedicineBreastfeedingPregnancyBreast milkDiseaseClinical trialRetinopathy of prematurityPediatricsVEGF receptorsDiabetic retinopathyIntensive care medicineObstetricsDiabetes mellitusInternal medicineEndocrinologyGestational age

Abstract

fetched live from OpenAlex

Anti-vascular endothelial growth factor (VEGF) is the mainstay of treatment for several visually debilitating diseases and is considered the standard of care for a number of conditions which may affect younger patients, including women of childbearing age. These commonly include, but are not restricted to, diabetic macular edema (DME), proliferative diabetic retinopathy (PDR) and myopic choroidal neovascularization (CNV). As in other areas of medicine, pregnant and breastfeeding women are often excluded from clinical trials due to the unknown side effect profile of new drugs. This lack of evidence regarding the safety of anti-VEGF agents in pregnancy and breastfeeding introduces challenges for clinicians seeking to counsel these patients, particularly because anti-VEGF injections may be often used for an extended period of time, depending on the nature of the retinal disease. As a precaution, anti-VEGF injections are generally not recommended for women who are either pregnant or breastfeeding, given that they are considered Category C drugs and there is limited data regarding their excretion in human breast milk. Therefore, treatment of this group of patients is typically managed on a case-by-case basis, balancing the potential patient benefits with safety concerns for the infant.

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 score0.185
Threshold uncertainty score0.995

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 head0.022
GPT teacher head0.270
Teacher spread0.247 · 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
Published2023
Admission routes1
Has abstractyes

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