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Record W2422935378 · doi:10.1017/cjn.2016.150

P.046 Can targeted exercises for nerve movement be effective for primary restless leg syndrome in adults with and without musculoskeletal pain?

2016· article· en· W2422935378 on OpenAlexvenueno aff
SG Gibbons

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2016
Typearticle
Languageen
FieldMedicine
TopicRestless Legs Syndrome Research
Canadian institutionsnot available
Fundersnot available
KeywordsRestless legs syndromePhysical therapyMedicinePhysical medicine and rehabilitationRating scaleStraight leg raiseRange of motionPsychologyNeurology

Abstract

fetched live from OpenAlex

Background: Restless leg syndrome (RLS) is common with musculoskeletal pain conditions and has been associated with small fiber neuropathy. There are few reports of non pharmacological management of RLS. The purpose of this paper was to report the use nerve mobilization exercises in a group of patients with primary RLS with and without co-morbid chronic non specific low back pain (LBP). Methods: 26 consecutive patients (11M/14F) with primary RLS and LBP attended a mean of 12 physiotherapy sessions (range 4-16). Patients were given 3 neural mobilization exercises to do twice daily 15-20 repetitions. Outcome measures were: Global Rating of Change Scale (GROC); Restless Legs Syndrome Rating Scale (RLS-RS); and RLS Ordinal Scale (RLS-OS). Based on the RLS-RS 1 was very severe, 8 were severe and 17 were moderate. Results: Follow up was a mean of 14 months (range12-16). Mean baseline for the RLS-RS was 22.8. The mean change was 20.3 (range 14-26). The mean baseline for the RLS-OS was 4.3. The mean score at follow up was 1.2 (range 1-4). GROC changed a mean of 6.2 (range 3-7). Conclusions: The results suggest that targeted exercises may be useful in managing primary RLS. A level 1 clinical trial is warranted. Further research is needed to identify the mechanism of action.

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)0.007
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.006
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.295
Teacher spread0.270 · 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.

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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences NeurologiquesSame topicRestless Legs Syndrome ResearchFrench-language works237,207