Intrasession reliability and influence of breathing during clinical assessment of lumbar spine postural control
Bibliographic record
Abstract
The aims of this study were to evaluate the influence of breathing when measuring lumbar postural control during a clinical progressive lumbar stabilization test (LST) and to estimate the intrasession reliability of the LST. The lumbar postural control index was calculated by using a biofeedback pressure unit. The LST was performed in two different positions (crook lying and upright) and two respiratory conditions (apnea and breathing) by 20 healthy individuals. The intrasession reliability of the lumbar postural control index of one trial was estimated with intraclass correlation coefficient (ICC) based on an Anova model. The results showed that the lumbar postural control index is similar between testing positions. There is an increase of the lumbar postural control index during breathing compared to the apnea. The reliability of the lumbar postural control index was fair to good (ICC 0.28-0.58). We also found that for the apnea, three trials had to be averaged to attain an ICC of 0.80 for both positions. The results of the present study indicate that the progressive LST can be similarly conducted in either supine or upright posture. Clinicians should be aware of the influence of breathing during LST. However, breathing could also serve as a clinical strategy to challenge lumbar spine postural control and stability during bracing therapeutic exercises.
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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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".