MétaCan
Menu
Back to cohort
Record W4386208782 · doi:10.1002/dvdy.650

Neural dependency in wound healing and regeneration

2023· review· en· W4386208782 on OpenAlexafffund
Alexandra Noble, Rozana Qubrosi, Solsa Cariba, Kayla J Favaro, Samantha L. Payne

Bibliographic record

VenueDevelopmental Dynamics · 2023
Typereview
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRegeneration (biology)BlastemaBiologyWound healingCell biologyNeuroscienceVertebratePeripheral nerveAnatomyImmunology

Abstract

fetched live from OpenAlex

In response to injury, humans and many other mammals form a fibrous scar that lacks the structure and function of the original tissue, whereas other vertebrate species can spontaneously regenerate damaged tissues and structures. Peripheral nerves have been identified as essential mediators of wound healing and regeneration in both mammalian and nonmammalian systems, interacting with the milieu of cells and biochemical signals present in the post-injury microenvironment. This review examines the diverse functions of peripheral nerves in tissue repair and regeneration, specifically during the processes of wound healing, blastema formation, and organ repair. We compare available evidence in mammalian and nonmammalian models, identifying critical nerve-mediated mechanisms for regeneration and providing future perspectives toward integrating these mechanisms into a therapeutic framework to promote regeneration.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.098
GPT teacher head0.344
Teacher spread0.245 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations15
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueDevelopmental DynamicsSame topicNerve injury and regenerationFrench-language works237,207