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Record W2340084435 · doi:10.1016/j.gendis.2016.03.003

Seeing is believing: Stem cells to treat blindness

2016· article· en· W2340084435 on OpenAlexafffund
Fei Li, Jim Hu

Bibliographic record

VenueGenes & Diseases · 2016
Typearticle
Languageen
FieldMedicine
TopicCorneal Surgery and Treatments
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersCystic Fibrosis CanadaCanadian Institutes of Health ResearchCystic Fibrosis Foundation TherapeuticsCystic Fibrosis Foundation
KeywordsCorneaCataractsBlindnessLens (geology)Regeneration (biology)Transparency (behavior)Stem cellTransplantationMedicineOptometryOphthalmologySurgeryBiologyCell biologyComputer scienceComputer security

Abstract

fetched live from OpenAlex

The majority of clinical blindness is caused by a loss of transparency of the lens and cornea, largely due to cataracts and corneal injuries. The most common treatment used to restore the transparency is surgical removal of the damaged tissues, followed by transplantation of donated corneal tissue or an artificial lens. However, these therapies are not without limitations or untoward effects. Unraveling the intricate regulatory signals required for cornea and lens development has made it possible to harness the lineage growth potential of stem cells for cornea repair and lens regeneration, as showcased in two recent studies published in the March 17th issue of Nature.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.948

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)0.0010.001

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 head0.027
GPT teacher head0.272
Teacher spread0.244 · 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

Citations0
Published2016
Admission routes2
Has abstractyes

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