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Record W2157188036 · doi:10.1186/1471-2474-8-118

The effectiveness of motorised lumbar traction in the management of LBP with lumbo sacral nerve root involvement: a feasibility study

2007· article· en· W2157188036 on OpenAlexaboutno aff
Annette Harte, Jacqueline H. Gracey

Bibliographic record

VenueBMC Musculoskeletal Disorders · 2007
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePhysical therapyRandomized controlled trialSports medicineLow back painNerve rootOrthopedic surgeryRehabilitationTraction (geology)RheumatologySciaticaPhysical medicine and rehabilitationSurgeryAlternative medicineInternal medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Traction is commonly used for the treatment of low back pain (LBP), predominately with nerve root involvement; however its benefits remain to be established. The aim of this study was to test the feasibility of a pragmatic randomized controlled trial to compare the difference between two treatment protocols (manual therapy, exercise and advice, with or without traction) in the management of acute/sub acute LBP with 'nerve root' involvement. METHODS: 30 LBP patients with nerve root pain were recruited and randomly assigned to one of two treatment groups. Primary outcome measures were the: McGill pain questionnaire, Roland Morris disability questionnaire, and the SF36 Questionnaire; recorded at baseline, discharge, 3 and 6 months post-discharge. RESULTS: 27 patients completed treatment with a loss of another four patients at follow up. Intention to treat analysis demonstrated an improvement in all outcomes at follow up points but there appeared to be little difference between the groups. CONCLUSION: This study has shown that a trial recruiting patients with 'nerve root' problems is feasible. Further research based upon a fully powered trial is required to ascertain if the addition of traction has any benefit in the management of these patients. REGISTRATION NUMBER: ISRCTN78417198.

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)0.003
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 score0.012
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.018
GPT teacher head0.315
Teacher spread0.297 · 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

Citations15
Published2007
Admission routes1
Has abstractyes

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