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Record W7014675319

Prevalência de dor no ombro em atletas de natação

2021· article· pt· W7014675319 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2021
Typearticle
Languagept
FieldHealth Professions
TopicOccupational health in dentistry
Canadian institutionsnot available
Fundersnot available
KeywordsMotor activityAthletesShouldersBody weight
DOInot available

Abstract

fetched live from OpenAlex

Introdução: O alto volume de treino e movimentos em amplitudes extremas na natação geram um alto estresse sobre as estruturas articulares e musculoesqueléticas do atleta, sendo comum as queixas de dores articulares em atletas de natação. Objetivo: Identificar a prevalência de dor no ombro em nadadores federados do estado do Rio de Janeiro na categoria master. Materiais e métodos: 61 atletas masculinos da categoria master, com faixa etária de 51,26 ±15,99 anos, participaram deste estudo. Estes competidores de natação federados por clubes participaram da II Etapa do Circuito Estadual Master de Natação, promovido pela FARJ em 2019. A coleta de dados foi realizada a partir da aplicação do questionário McGill reduzido e da EVA (Escala Visual Analógica). Resultados: A prevalência de dor foi de 73,8%. Na dimensão sensorial, o descritor cálico obteve a menor média (0,08) e o descritor dolorida apresentou a maior (0,74). Na dimensão afetiva, o descritor amedrontado obteve a menor média (0,13), e o descritor cansativo/exaustiva a maior média (0,38). A média da EVA (1,94 ±2,13) sugere que os ní­veis de dor foram baixos. Conclusão: Embora a alta prevalência de dor identificada, os atletas participantes deste estudo apresentaram baixo ní­vel de dor no ombro.

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)0.008
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0010.003
Open science0.0050.004
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.1540.002

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 head0.467
GPT teacher head0.682
Teacher spread0.214 · 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; both teacher heads agree on what is shown here.

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
Published2021
Admission routes1
Has abstractyes

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