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Normalizing Foot Moment Arm Lengths in Men and Women

2015· article· en· W2461921631 on OpenAlexaff
Lawrence W. Weiss, Johnpaul Caia, Loren Z.F. Chiu, Brian K. Schilling, Max R. Paquette

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2015
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCalipersMathematicsStandard errorStandard deviationIntraclass correlationOrthodonticsFoot (prosody)AnthropometryInterclass correlationAnatomyMedicineStatisticsGeometryReproducibility

Abstract

fetched live from OpenAlex

Foot anthropometric dimensions may influence moment arm lengths and respective moments of force resisted by skeletal muscles. Depending upon circumstances, absolute or normalized expressions of these measures may have greater utility. PURPOSE: To determine the stability reliability and precision of two absolute and normalized moment arm lengths of the foot. METHODS: Longitudinal foot dimensions were obtained in 27 men and 27 women including the anterior-posterior distance between the posterior calcaneus and: 1) talocrural (TALO) and 2) metatarsophalangeal (META) joints. Bilateral measurements of seated subjects were performed using a digital sliding caliper. Absolute dimensions were normalized to height and lower-limb length (LLL, greater trochanter to lateral malleolus). Measures were obtained twice, separated by either 24 or 48 hours. Stability reliability was determined using intraclass correlation coefficient (ICC), and precision using standard error of measurement (SEM). RESULTS: The scatter of points around the trend lines for all 54 subjects combined appear to be uniform and the separate SEMs for men and women are similar. Assuming homoscedasticity, data are reported for the combined groups. For the 3 bilateral expressions of the 2 foot dimensions (12 measures), the low and high means (standard deviation) are reported for each session as well as the ICC and SEM for the pair of sessions. For TALO, means were 5.1 (0.4) and 5.2 (0.5) cm, ICCs were 0.79 and 0.82, and SEMs were 0.2 and 0.2. When normalized to height, means were 0.030 (0.002) and 0.030 (0.002) cm/cm height, ICCs were 0.65 and 0.75, and SEMs were 0.001 and 0.001. When normalized to LLL, means were 0.062 (0.005) and 0.063 (0.005) cm/cm LLL, ICCs were 0.69 and 0.73, and SEMs were 0.003 and 0.003. For META, means were 17.0 (1.2) and 17.1 (1.1) cm, ICCs were 0.97 and 0.98, and SEMs were 0.2 and 0.2. When normalized to height, means were 0.098 (0.002) and 0.100 (0.002) cm/cm height, ICCs were 0.92 and 0.94, and SEMs were 0.001 and 0.001. When normalized to LLL, means were 0.205 (0.010) and 0.208 (0.011) cm/cm LLL, ICCs were 0.92 and 0.92, and SEMs were 0.003 and 0.003. CONCLUSIONS: Surface landmarks and digital calipers can be used to precisely measure the 2 foot dimensions in question, whether or not they are normalized. Reliability is high for the META dimension, and lower for the TALO dimension. Meticulous data acquisition protocols are required to acquire potentially useful data.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
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)0.0010.000

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 head0.021
GPT teacher head0.250
Teacher spread0.230 · 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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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