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An Anatomical Comparative Study of Lower Limb Discrepancy between Inuit and Urban Population Groups

2008· article· en· W2464320022 on OpenAlexaboutno aff
Natalie B. Ormond, Samuel Márquez, Christen Russo, Noel Caceres

Bibliographic record

VenueThe FASEB Journal · 2008
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal Disorders and Rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsFemurMedicineTibiaPopulationRehabilitationDominance (genetics)GaitAnatomyPhysical therapySurgeryBiology

Abstract

fetched live from OpenAlex

Lower limb (LL) discrepancy (LLD), frequently found within the general population (P), afflicts a wide spectrum of polytypic individuals. This condition has been linked to biomechanical abnormalities that alter posture causing kinematic changes in gait. This study compares bones of LL in two different Ps with distinct biomechanical behaviors: Inuit (I) and an Urban population (UP). Donor (n=23) & I (n=28) specimens were used from the College of Health Related Professions at SUNY Downstate anatomy program & from Museum collections. Femora & tibia were disarticulated and measured. Paired t‐test on UP revealed differences of femora with a dominance of right (46.6 cm +/− 0.4) over left (45.9 cm +/− 0.5) LL lengths from the mixed‐sex sample (n=23, p<0.05). Paired t‐test revealed femoral differences (p<0.05) with a dominance of male (48.4 cm +/− 0.7) over female (44.1 cm +/− 0.8). A mixed linear model tested for the interaction of sex on side showed males (p<0.05) having a more significant effect over females (p=0.05) but not the reverse. Femur and tibial measures were different for the I P scoring p< 0.05 for both bones. The rehabilitation perspective of PTs require a thorough knowledge of the effects of LLD that set off the different types of compensatory mechanisms employed by the patient. The appropriate therapeutic strategies can be strengthened with a more thorough knowledge of the underlying bony anatomy.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.012
Threshold uncertainty score0.200

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.027
GPT teacher head0.313
Teacher spread0.286 · 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
Published2008
Admission routes1
Has abstractyes

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