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Record W2053456123 · doi:10.1016/j.jmpt.2008.09.003

Differences in Range of Motion Between Dominant and Nondominant Sides of Upper and Lower Extremities

2008· article· en· W2053456123 on OpenAlexaff
Luciana Macedo, David J. Magee

Bibliographic record

VenueJournal of Manipulative and Physiological Therapeutics · 2008
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineRange of motionAnkleWristElbowGoniometerPhysical medicine and rehabilitationAnkle dorsiflexionSignificant differencePhysical therapyOrthodonticsSurgeryInternal medicineMathematics

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this study was to compare ranges of motion (ROM) between dominant and nondominant sides for the joints of the upper and lower extremities. METHODS: Ninety healthy white women from 18 to 59 years of age were measured in this study. Active and passive ROM were measured for the ankle, knee, hip, shoulder, elbow, and wrist using a standard goniometer. The order of the joints, motion, sides, and active or passive motion testing was randomly selected. A paired t test was used for the comparison between sides. RESULTS: The results of this study showed a statistically significant difference between dominant and nondominant sides for 34 of the 60 ROM measured. The maximum mean difference between sides for all ROM measured was 7.5 degrees . CONCLUSION: The results of this show that some ROM are different between body sides and that when these differences exist they are minimal and may not be clinically insignificant. These results support the practice of using the opposite side of the body as an indicator of preinjury or normal extremity ROM.

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)0.002
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0040.001

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 head0.269
GPT teacher head0.337
Teacher spread0.068 · 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

Citations87
Published2008
Admission routes1
Has abstractyes

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