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Record W2110201426 · doi:10.1002/mus.10374

Ankle kinematics to evaluate functional recovery in crushed rat sciatic nerve

2003· article· en· W2110201426 on OpenAlexaff
Artur S.P. Varejão, António Cabrita, Marcel F. Meek, José Bulas‐Cruz, Vítor Filipe, Ronaldo Gabriel, António Ferreira, Stefano Geuna, David A. Winter

Bibliographic record

VenueMuscle & Nerve · 2003
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAnkleKinematicsPhysical medicine and rehabilitationSciatic nerveMedicineCrush injuryAnalysis of varianceLesionRepeated measures designAnatomySurgeryMathematicsPhysicsInternal medicine

Abstract

fetched live from OpenAlex

Peripheral nerve researchers frequently use the rat sciatic nerve crush model in order to test different therapeutic approaches. The purpose of this study was to determine the sequence of changes after an axonotmetic injury by means of a biomechanical model of the foot and ankle, and compare them with walking track analysis, over a fixed period of time. A kinematic analysis program was used to acquire ankle motion data for further analysis. Although repeated measures analysis of variance showed significant cumulative changes induced by the crush lesion for both ankle kinematic parameters and sciatic functional index, post-hoc multiple comparisons by the Student-Neuman-Keuls test revealed significant differences between week 0 and week 8 only for ankle kinematics. These results are of importance in showing the superiority of ankle kinematics in detecting small biomechanical deficits related to hyperexcitability of the plantarflexor muscles, in contrast with walking track analysis, which showed full motor functional recovery 8 weeks after the crush lesion.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.054
GPT teacher head0.284
Teacher spread0.230 · 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 designBench or experimental
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

Citations68
Published2003
Admission routes1
Has abstractyes

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