MétaCan
Menu
Back to cohort
Record W4415014464 · doi:10.1016/j.jcm.2025.09.020

Kinematic Evaluation of Sagittal Spine Motion During Walking With Internal Frame and Frameless Backpacks

2025· article· en· W4415014464 on OpenAlexaff
Laura Bryson, J. Bryson, Brent S. Russell, Ronald S. Hosek

Bibliographic record

VenueJournal of Chiropractic Medicine · 2025
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsCanadian Chiropractic Association
Fundersnot available
KeywordsBackpackSagittal planeKinematicsTreadmillLumbarLumbar spineChiropracticBiomechanicsGait

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.060
GPT teacher head0.470
Teacher spread0.410 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Chiropractic MedicineSame topicOccupational Health and PerformanceFrench-language works237,207