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Record W6910394439 · doi:10.48448/zth7-j006

Cellular senescence and retinal vascular disease

2021· other· en· W6910394439 on OpenAlexaboutno aff

Bibliographic record

VenueUnderline Science Inc. · 2021
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsRetinalDiabetic retinopathyPathologicalSenescenceAngiogenesisNeovascularizationRetinopathy

Abstract

fetched live from OpenAlex

Authors: Przemyslaw (Mike) Sapieha¹, Mike (Przemyslaw) Sapieha¹ ¹University of Montreal Abstract: Pathological retinal neovascularization is the hallmark of primary blinding diseases across all age groups, yet surprisingly little is known about the causative factors. These diseases include diabetic retinopathy and retinopathy of prematurity where progressive decay of retinal vasculature yields zones of neural ischemia. These avascular zones and the hypoxic neurons and glia that reside in them are the source of pro-angiogenic factors that mediate destructive pre-retinal angiogenesis. This lecture will focus on how cellular senescence, a dynamic and typically terminal cellular response to various stressors often associated with aging, influences progression of retinopathies. Specifically, the contribution of cellular senescence to pathological angiogenesis and vascular remodeling will be discussed.

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)0.000
metaresearch head score (Gemma)0.000
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: Review · Consensus signal: Review
Teacher disagreement score0.034
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0340.012

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 head0.016
GPT teacher head0.266
Teacher spread0.249 · 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
GenreReview

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
Published2021
Admission routes1
Has abstractyes

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