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Record W3005769585 · doi:10.23750/abm.v90i3.7236

Mobilization of the contralateral limb in Slump position: effects on knee extension in healthy adult subjects.

2019· article· en· W3005769585 on OpenAlexaff
Leonardo Pellicciari, Matteo Paci, Tommaso Geri, Daniele Piscitelli, Marco Baccini

Bibliographic record

VenuePubMed · 2019
Typearticle
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsMcGill University
FundersMinistero della Salute
KeywordsMedicineRange of motionPlaceboCondyleMobilizationSurgeryPhysical medicine and rehabilitationAnesthesia

Abstract

fetched live from OpenAlex

BACKGROUND AND AIM: In the acute phase of neuropathic pain due to nerve root disorders, the neurodynamic approach proposes the mobilization of the contralateral limb to decrease the pain and increase the range of motion in the affected limb. The aim of this study was to evaluate the effect of the contralateral knee mobilization on the ipsilateral knee extension range of motion in Slump position in healthy adult subjects. METHODS: Thirty-eight healthy subjects underwent a placebo, control and experimental manoeuvres that included a passive contralateral knee mobilization into extension. The knee, hip and cervical angles and distance between glabella and femoral condyle achieved in Slump test position were measured with an optoelectronic motion analysis system before and after each manoeuvre. RESULTS: Experimental manoeuvre produced a statistically significant increase of the knee extension ROM when compared to the control (p=.017) and placebo (p=.007) manoeuvres. A significant increase of the hip angle and distance between glabella and femoral condyle was detected after the experimental manoeuvres (p<.001), but not after the placebo and control manoeuvres. CONCLUSIONS: The contralateral mobilization in Slump position increases the ipsilateral knee extension ROM. Further research is required to confirm that the knee increment ROM was due to the neural component.

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 score0.042
Threshold uncertainty score0.296

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.224
Teacher spread0.217 · 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 routes1
Has abstractyes

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