Kinematic Evaluation of Sagittal Spine Motion During Walking With Internal Frame and Frameless Backpacks
Bibliographic record
Abstract
Objective: The purpose of this study was to examine sagittal spinal movements during walking with frameless and internal frame backpacks. Methods: Twenty adult students from a chiropractic college walked on a treadmill while wearing inertial measurement units mounted on the head, T1 and T12 vertebrae, and sacrum. They walked without a backpack, with a backpack loaded with 6.8 kg, and after 15 minutes walking with the loaded pack. Day 1, they wore a backpack with no structural support; day 2, they wore an internal frame backpack with a hip belt and chest compression straps. Cyclic sagittal flexion and extension endpoints were identified using an application written in the R language. Results: Participants walked with the lumbar spine in slight flexion throughout all trials with both packs; flexion significantly increased with load and with time, more so with the frameless pack. The thoracic region showed progressive extension with load and time, significant only for the frameless pack. The cervical region trended toward progressive extension with load and time, with no significant changes for either pack. Conclusion: In this group of adult students walking on a treadmill, there were trends of increasing lumbar flexion, thoracic extension, and cervical extension, with changes more pronounced with a frameless backpack than one with an internal frame.
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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".