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Record W2129254049 · doi:10.1126/scitranslmed.3003688

Building Vascular Networks

2012· review· en· W2129254049 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScience Translational Medicine · 2012
Typereview
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversité Laval
FundersNational Institute of Dental and Craniofacial ResearchNational Institute of Biomedical Imaging and BioengineeringNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute of General Medical SciencesNational Cancer InstituteNational Heart, Lung, and Blood Institute
KeywordsMedicine

Abstract

fetched live from OpenAlex

Only a few engineered tissues-skin, cartilage, bladder-have achieved clinical success, and biomaterials designed to replace more complex organs are still far from commercial availability. This gap exists in part because biomaterials lack a vascular network to transfer the oxygen and nutrients necessary for survival and integration after transplantation. Thus, generation of a functional vasculature is essential to the clinical success of engineered tissue constructs and remains a key challenge for regenerative medicine. In this Perspective, we discuss recent advances in vascularization of biomaterials through the use of biochemical modification, exogenous cells, or microengineering technology.

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.

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)0.003
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.870

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.086
GPT teacher head0.390
Teacher spread0.303 · 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