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Record W3127814007 · doi:10.1016/j.jpha.2021.02.001

Printability–A key issue in extrusion-based bioprinting

2021· review· en· W3127814007 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Pharmaceutical Analysis · 2021
Typereview
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsExtrusion3D bioprintingScaffoldNanotechnology3D printingProcess (computing)Tissue engineeringComputer scienceMaterials scienceBiomedical engineeringEngineeringComposite material

Abstract

fetched live from OpenAlex

Three-dimensional (3D) extrusion-based bioprinting is widely used in tissue engineering and regenerative medicine to create cell-incorporated constructs or scaffolds based on the extrusion technique. One critical issue in 3D extrusion-based bioprinting is printability or the capability to form and maintain reproducible 3D scaffolds from bioink (a mixture of biomaterials and cells). Research shows that printability can be affected by many factors or parameters, including those associated with the bioink, printing process, and scaffold design, but these are far from certain. This review highlights recent developments in the printability assessment of extrusion-based bioprinting with a focus on the definition of printability, printability measurements and characterization, and printability-affecting factors. Key issues and challenges related to printability are also identified and discussed, along with approaches or strategies for improving printability in extrusion-based bioprinting.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.006
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.080
GPT teacher head0.440
Teacher spread0.360 · 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