MECHANICAL TRACTION FOR MECHANICAL NECK DISORDERS: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
OBJECTIVE: To assess whether mechanical traction, either alone or in combination with other treatments, improves pain, function/disability, patient satisfaction and global perceived effect in adults with mechanical neck disorders. METHODS: We conducted a systematic review up to September 2004 of randomized controlled trials and used pre-defined levels of evidence for qualitative analysis. Two independent reviewers conducted study selection, data abstraction and methodological quality assessment. Using a random effects model, relative risk and standardized mean differences were calculated. The reasonableness of combining studies was assessed on clinical and statistical grounds. In the absence of heterogeneity, pooled effect measures were calculated. RESULTS: Of the 10 selected trials, one study was of high quality. Our review revealed low-quality trials for mechanical neck disorders, showing evidence of benefit favouring intermittent traction for pain reduction. Continuous traction showed no significant difference for defined outcomes. CONCLUSION: Inconclusive evidence for continuous and intermittent traction exists due to trial methodological quality. Two clinical conclusions may be drawn, one favouring the use of intermittent traction and the other not supporting the use of continuous traction. Attention to research design flaws and description of traction characteristics is needed.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".