Influence of Mckenzie protocol and two modes of endurance exercises on health-related quality of life of patients with long-term mechanical low-back pain.
Bibliographic record
Abstract
INTRODUCTION: Long-term Mechanical Low-Back Pain (LMLBP) negatively impacts on patients' physical capacity and quality of life. This study investigated the relationship between Health-Related Quality of Life (HRQoL) and pain intensity, and the influence of static and dynamic back extensors' endurance exercises on HRQoL in Nigerian patients with LMLBP treated with the McKenzie Protocol (MP). METHODS: A single-blind controlled trial involving 84 patients who received treatment thrice weekly for eight weeks was conducted. Participants were assigned to the MP Group (MPG), MP plus Static Back Endurance Exercise Group (MPSBEEG) or MP plus Dynamic Endurance Exercise Group (MPDBEEG) using permuted randomization. HRQoL and pain was assessed using the Short-Form (SF-36) questionnaire and Quadruple Visual Analogue Scale respectively. RESULTS: Sixty seven participants aged 51.8 ± 7.35 years completed the study. A total drop-out rate of 20.2% was observed in the study. Within-group comparison across weeks 0-4, 4-8 and 0-8 of the study revealed significant differences in HRQoL scores (p < 0.05). Treatment Effect Scores (TES) across the groups were significantly different (p = 0.001). MPSBEEG and MPDBEEG were comparable in TES on General Health Perception (GHP) at week 4; and GHP and Physical Functioning at week 8 respectively (p > 0.05). However, MPDEEG had significantly higher TES in the other domains of the SF-36 (p = 0.001). CONCLUSION: HRQoL in patients with LMLBP decreases with pain severity. Each of MP, static and dynamic back extensors endurance exercises significantly improved HRQoL in LMLBP. However, the addition of dynamic back extensors endurance exercise to MP led to greater improvement in HRQoL.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".