The Impact of Participant Instructions on Lumbopelvic Rhythm During Forward Spine Flexion and Return
Bibliographic record
Abstract
Spine motion and outcome measures derived therein may be confounded by the motion instructions provided to participants during data collection. This observational analytical laboratory-controlled study explored the impact of instructions for forward flexion trials on the lumbopelvic ratio (LPR) and spine kinematics. Twenty-five participants (mean age: 25.68 [9.31] y) performed forward bending trials with general instructions and then specific instructions to avoid hip motion and follow pacing. Participants were equipped with triaxial accelerometers at the lumbar spine (first lumbar vertebra) and pelvis (second sacral vertebra) to measure lumbar and pelvic excursions and LPR across the movement phases. The Wilcoxon signed-rank test was used to compare outcome variables between conditions. Lumbar excursion did not differ significantly between conditions, whereas pelvic excursion was significantly reduced in the specific instructions condition (P < .05). LPR showed considerable variability in the specific instructions condition, but there was no significant difference from the general instruction condition. Thus, instructing participants to limit hip motion appears to reduce pelvic excursion during forward bending without statistically affecting lumbar motion or the LPR. Providing and reporting clear and precise movement instructions to participants is important as they can change kinematics. Further, it appears that verbal instructions alone are unlikely to achieve complete spine movement isolation in all participants.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".