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Record W2947514683 · doi:10.3390/pr7060336

Advancements in Canadian Biomaterials Research in Neurotraumatic Diagnosis and Therapies

2019· article· en· W2947514683 on OpenAlexafffundabout
Suzan Chen, Angela M. Auriat, Tongda Li, Taisa Regina Stumpf, Ryan G. Wylie, Daniel Chen, Stephanie M. Willerth, Maria C. DeRosa, Maryam Tarizian, Xudong Cao, Eve C. Tsai

Bibliographic record

VenueProcesses · 2019
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsCarleton UniversityMcGill UniversityUniversity of VictoriaUniversity of SaskatchewanMcMaster UniversityUniversity of OttawaOttawa Hospital
FundersOttawa Hospital Research InstituteUniversity of Ottawa
KeywordsScaffoldBiomaterialMedicineDrug deliveryNeuroscienceIntensive care medicineNanotechnologyBiomedical engineeringMaterials sciencePsychology

Abstract

fetched live from OpenAlex

Development of biomaterials for the diagnosis and treatment of neurotraumatic ailments has been significantly advanced with our deepened knowledge of the pathophysiology of neurotrauma. Canadian research in the fields of biomaterial-based contrast agents, non-invasive axonal tracing, non-invasive scaffold imaging, scaffold patterning, 3D printed scaffolds, and drug delivery are conquering barriers to patient diagnosis and treatment for traumatic injuries to the nervous system. This review highlights some of the highly interdisciplinary Canadian research in biomaterials with a focus on neurotrauma applications.

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 score0.046
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.090
GPT teacher head0.371
Teacher spread0.281 · 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

Citations2
Published2019
Admission routes3
Has abstractyes

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