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Record W6992002428

An investigation into the effect of a high velocity low amplitude manipulation on core muscle strength in patients with chronic mechanical lower back pain

2008· dissertation· en· W6992002428 on OpenAlexaboutno aff

Bibliographic record

VenueDUT Open Scholar (Durban University of Technology) · 2008
Typedissertation
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsCore stabilityLow back painCore (optical fiber)Back painChiropracticLower limbChronic pain
DOInot available

Abstract

fetched live from OpenAlex

Brunarski (1984) says that philosophically and historically, chiropractic has been uniquely orientated toward an emphasis on preventative care and health maintenance with a mechanistic and hands-on model for treatment.Instead of reductionism, chiropractors focus on holism, non-invasiveness and the sharing of the responsibilities for healing between doctor and patient.As stated in a Canadian report by Manga et al. (1993), lower back pain is a ubiquitous problem and there are many epidemiological and statistical studies documenting the high incidence and prevalence of lower back pain (Manga et al., 1993).Evans and Oldreive (2000) revealed in a study of the transversus abdominis that low back pain patients had reduced endurance of the transverses abdominis and that its protective ability was decreased.In addition, it was noted that wasting and inhibition of the other core stabiliser and co-contractor, multifidus, was present (Hides et al.,1994), both of which have been linked to the presence of low back pain (Evans andOldreive, 2000 andHides et al., 1994).Thus, it stands to reason that manipulation, as an effective treatment for low back pain (Di Fabio, 1992), could be effective in restoring the strength and endurance of the core stability muscles.This is theoretically supported by the fact that a restriction in motion and pain due to mechanical derangement in the low back can be effectively treated by manipulation

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)0.001
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 score0.070
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.007
GPT teacher head0.233
Teacher spread0.226 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

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