Evaluation of the Lumbar Kinematic Measures That Most Consistently Characterize Lumbar Muscle Activation Patterns During Trunk Flexion: A Cross-Sectional Study
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to determine which kinematic measure most consistently determined onset and cessation of the flexion-relaxation response. METHODS: The study was a cross-sectional design in a laboratory setting in which 20 asymptomatic university-aged (19.8-33.3 years old) participants were tested. Muscle activation was measured for the lumbar erector spinae, and 3-dimensional motion was recorded. Flexion-relaxation onset and cessation occurrences were determined for 10 standing maximum voluntary flexion trials. The lumbar and trunk angles at both events were expressed as unnormalized (°) and normalized (%Max: percentage of maximum voluntary flexion) measures. Intraclass correlation coefficients and coefficients of variation were calculated to determine within- and between-participant reliability, respectively. RESULTS: Mean (SD) unnormalized flexion-relaxation angles ranged from 46.28° (11.63) (lumbar onset) to 108.10° (12.26) (trunk cessation), whereas normalized angles ranged from 71.31%Max (16.44) (trunk onset) to 94.83%Max (lumbar cessation). Intraclass correlation coefficients ranged from 0.905 (normalized lumbar, left side, onset) to 0.995 (unnormalized lumbar, both sides, cessation). Coefficients of variation ranged from 3.56% (normalized lumbar, right side, cessation) to 26.02% (unnormalized trunk, left side, onset). CONCLUSIONS: The data suggest that, for asymptomatic individuals, unnormalized and normalized lumbar kinematics most consistently characterized flexion-relaxation angles within and between participants, respectively. Lumbar measures may be preferential when the flexion-relaxation response is investigated in future clinical and biomechanical studies.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".