An investigation into the effect of a high velocity low amplitude manipulation on core muscle strength in patients with chronic mechanical lower back pain
Bibliographic record
Abstract
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 (Sandoz, 1976; Korr (Leach, 1994); Herzog et al., 1999;Homewood, 1979;Vernon andMrozek, 2005 and Wyke (Leach, 1994)).Homewood (1979) described that a subluxation may interfere with the nerve supply and result in a decrease in muscular activity.He hypothesized that removal of the subluxation could restore: normal physiological processes, increase muscle activity and; improve functional ability and normalize the torque ratios (Herzog et al., 1999;
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".