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Record W2442394007 · doi:10.1177/107110070202301207

Toe Flexor Forces in Dancers and Non-Dancers

2002· article· en· W2442394007 on OpenAlexaff
Aneel Nihal, Jeffrey A. Goldstein, Judith Haas, Rudi Hiebert, Frederick J. Kummer, Marijeanne Liederbach, Elly Trepman

Bibliographic record

VenueFoot & Ankle International · 2002
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversity of Manitoba
FundersSchool of Medicine, New York UniversityJohn Charnley Trust
KeywordsMedicineSittingFlexor musclesPhysical therapyPhysical medicine and rehabilitationOrthodontics

Abstract

fetched live from OpenAlex

Toe flexor force (hallux and second toe) was determined in the right and left feet of 24 dancers and 29 non-dancers (sitting and standing positions) using a commercially-available pressure sensor connected to a voltmeter. For the hallux and second toe combined (all trials combined), average toe flexor force was slightly greater for dancers than non-dancers (dancers, 7 +/- 4 N; non-dancers, 6 +/- 4 N; P<0.049). For dancers and non-dancers combined (all trials), the average toe flexor force of the hallux was more than twice that of the second toe (hallux, 9 +/- 4 N; 2nd toe, 4 +/- 1 N; P<0.0001); average toe flexor force was slightly greater in standing than sitting positions (standing, 7 +/- 4 N; sitting, 6 +/- 3 N; P<0.0001); and the average toe flexor force was slightly greater for the right than left foot (right, 7 +/- 4 N; left, 6 +/- 4 N; P<0.012). The average toe flexor force was greatest for the first repetition and slightly decreased for the second and third repetitions (first repetition, 7 +/- 4 N; second and third repetitions each, 6 +/- 4 N; P<0.0013). Toe flexor force measurement may potentially be applicable to clinical practice as a guide to rehabilitation after injury or as a screening parameter for readiness to advance dance or other athletic training, performance, or competition.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.998

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)0.0030.000

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 head0.015
GPT teacher head0.280
Teacher spread0.265 · 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.

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

Citations25
Published2002
Admission routes1
Has abstractyes

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