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Record W4399195629

Comparison of Lumbopelvic Movement Patterns in People with and Without Low Back Pain During Stair Descending Task

2018· article· en· W4399195629 on OpenAlexaff
Neda Namnik, Reza Salehi, Mohammad Jafar Shaterzadeh Yazdi, Fateme Esfandiarpour, Mohammad Mehravar, Neda Orakifar

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2018
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTask (project management)Physical medicine and rehabilitationMovement (music)Low back painMedicinePhysical therapyPsychologyEngineeringPhysicsAlternative medicine
DOInot available

Abstract

fetched live from OpenAlex

Background: Decreased lumbar spine control may be associated with early and/ or excessive lumbopelvic motion with trunk and lower extremity movements during functional and daily activities. This study investigated differences in lumbopelvic movement patterns in people with and without low back pain (LBP) during a stair descending (SD) task. Methods: A total of 36 subjects, 18 females with non-specific chronic low back pain (NSCLBP) and 18 healthy females, participated in this study. A threedimensional motion capture system was used to record kinematics during the SD task. Results: The results showed that in the LBP group, the start-time of the lumbar muscles occurred early in the movement (P=0.015). Additionally, subjects with LBP showed excessive lumbar spine and pelvic movement during the SD task (P<0.05). Conclusion: LBP patients make early and excessive lumbopelvic movements during a SD task, and this can be an important factor contributing to the development or persistence of their LBP problem. This finding should be considered by clinicians when evaluating functional tasks as part of movementbased examinations and rehabilitation programs for people with LBP

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.104
GPT teacher head0.510
Teacher spread0.406 · 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 source (direct Gemma or distilled Codex), 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
Published2018
Admission routes1
Has abstractyes

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