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Musculoskeletal Injuries Among Young, Recreational, Female Dancers Before and After Dancing in Pointe Shoes

2002· article· en· W1999937015 on OpenAlexaffabout
Natasha M.A. Nunes, Jonathan Haddad, Doreen J. Bartlett, Katherine D. Obright

Bibliographic record

VenuePediatric Physical Therapy · 2002
Typearticle
Languageen
FieldPsychology
TopicDiversity and Impact of Dance
Canadian institutionsWestern University
Fundersnot available
KeywordsDanceBallet dancerPhysical therapyBalletAnkleMedicineGeneralizability theoryRecreationPsychologyArtSurgeryDevelopmental psychology

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was to determine the prevalence, number, and distribution of musculoskeletal injuries among young, recreational, female dancers before and after dancing in pointe shoes and to explore possible risk factors. METHODS: Thirty-one female, nonprofessional ballet dancers, eight to 20 years of age, were recruited from two dance studios in London, Ontario. Participants completed a descriptive questionnaire and reliable examiners performed stress, stability, and laxity tests. RESULTS: The prevalence of instability for nonpointe and pointe groups was 0% and 8% for the knee and 17% and 3% for the ankle, respectively (a nonsignificant difference). The mean number of painful sites was 1.3 (SD = 1.9) and 2.9 (SD = 2.1) for nonpointe and pointe groups, respectively (p = 0.04). The only variable that, in part, predicted the number of painful sites was the number of years of having danced ballet. CONCLUSIONS: The generalizability of these results is limited by the small sample size. Additional prospective research with larger samples, inclusion of dancers who are just beginning to dance in pointe shoes, and consideration of level of exposure and the intensity of both dancing and other physical activity is indicated before prevention programs can be planned and tested.

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 score0.021
Threshold uncertainty score0.625

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)0.0010.000

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 head0.015
GPT teacher head0.283
Teacher spread0.268 · 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

Citations26
Published2002
Admission routes2
Has abstractyes

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