Lumbar Intervertebral Kinematics During an Unstable Sitting Task and Its Association With Standing-Induced Low Back Pain
Bibliographic record
Abstract
People developing transient low back pain during standing have altered control of their spine and hips during standing tasks, but the transfer of these responses to other tasks has not been assessed. This study used video fluoroscopy to assess lumbar spine intervertebral kinematics of people who do and do not develop standing-induced low back pain during a seated chair-tilting task. A total of 9 females and 8 males were categorized as pain developers (5 females and 3 males) or nonpain developers (4 females and 5 males) using a 2-hour standing exposure; pain developers reported transient low back pain and nonpain developers did not. Participants were imaged with sagittal plane fluoroscopy at 25 Hz while cyclically tilting their pelvises anteriorly and posteriorly on an unstable chair. Intervertebral angles, relative contributions, and anterior-posterior translations were measured for the L3/L4, L4/L5, and L5/S1 joints and compared between sexes, pain groups, joints, and tilting directions. Female pain developers experienced more extension in their L5/S1 joints in both tilting directions compared with female nonpain developers, a finding not present in males. The specificity in intervertebral kinematics to sex-pain group combinations suggests that these subgroups of pain developers and nonpain developers may implement different control strategies.
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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 | 0.000 |
| 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".