Exposure to Sustained Flexion Impacts Lumbar Extensor Spinae Muscle Fiber Orientation
Bibliographic record
Abstract
The lumbar extensor spinae (LES) has an oblique orientation with respect to the compressive axis of the lumbar spine, allowing it to counteract anterior shear forces. This mechanical advantage is lost as spine flexion angle increases. The LES orientation can also alter over time as obliquity decreases with age and is associated with decreased strength and low back pain. However, it is unknown if LES orientation is impacted by recent exposures causing adaptations over shorter timescales. Hence, the effects of a 10-minute sustained spine flexion exposure on LES orientation, thickness, and activity were investigated. Three different submaximally flexed spine postures were observed before and after the exposure. At baseline, orientation (P < .001) and thickness (P = .004) decreased with increasingly flexed postures. After the exposure, obliquity further decreased at low (pairwise comparison P < .001) and moderately (pairwise comparison P = .008) flexed postures. Low back creep occurred, but LES thickness did not change, indicating that decreases in orientation were not solely due to changes in muscle length at a given posture. Activation did not change to counteract decreases in obliquity. These changes encompass a reduced ability to offset anterior shear forces, thus increasing the potential risk of anterior shear-related injury or pain after low back creep-generating exposures.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".