Is assessing trunk muscle endurance in military with sub-acute and chronic low back pain clinically meaningful?
Bibliographic record
Abstract
Introduction Trunk muscle endurance (TME) tests are commonly used by clinicians to assess muscle performance changes in response to rehabilitation in patients with low back pain (LBP). The aim of this study was to assess the responsiveness of three TME-tests in patients with LBP and to evaluate the relationships between changes in TME and improvement in self-reported function. Materials and Methods Eighty-four LBP patients were evaluated at baseline and after completion of a 6-week training program. Function was assessed with the modified Oswestry Disability Index (ODI) while TME was estimated using three tests: (1) the Biering-Sørensen, (2) the side bridge endurance tests (both sides), and (3) the trunk flexor endurance test. The standardized response mean (SRM) and the minimal clinical important difference (MCID) for each TME-test, and the relationships between changes in TME and improvement in ODI were calculated. Results SRMs were small to large for TME-tests (range: 0.43–0.82), and large for the ODI (2.85) and no clinically useful MCID was identified for the TME-tests (area under the curve below 0.70). No significant correlations were found between changes in the TME and change in ODI scores (r < 0.15; all P > 0.05). Conclusion Our results show a weak responsiveness of TME-tests in patients with LBP. There was no association between endurance performance change and self-reported functional change. TME-tests may not be a key component of rehabilitation monitoring in patients with LBP.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".