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Record W2983461530 · doi:10.1002/mus.26760

Stem‐cell–based therapies to enhance peripheral nerve regeneration

2019· review· en· W2983461530 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

VenueMuscle & Nerve · 2019
Typereview
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
Fundersnot available
KeywordsRegeneration (biology)MedicinePeripheral nerve injuryPeripheral nerveStem cellNeuroscienceNerve injuryBioinformaticsPathologyAnatomySurgeryBiologyCell biology

Abstract

fetched live from OpenAlex

Peripheral nerve injury remains a major cause of morbidity in trauma patients. Despite advances in microsurgical techniques and improved understanding of nerve regeneration, obtaining satisfactory outcomes after peripheral nerve injury remains a difficult clinical problem. There is a growing body of evidence in preclinical animal studies demonstrating the supportive role of stem cells in peripheral nerve regeneration after injury. The characteristics of both mesoderm-derived and ectoderm-derived stem cell types and their role in peripheral nerve regeneration are discussed, specifically focusing on the presentation of both foundational laboratory studies and translational applications. The current state of clinical translation is presented, with an emphasis on both ethical considerations of using stems cells in humans and current governmental regulatory policies. Current advancements in cell-based therapies represent a promising future with regard to supporting nerve regeneration and achieving significant functional recovery after debilitating nerve injuries.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), 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 score0.947
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.072
GPT teacher head0.337
Teacher spread0.265 · 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