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Record W7119407326

Percezione del dolore nei motociclisti dilettanti

2020· article· pt· W7119407326 on OpenAlexaboutno aff
Karinne Machado de Souza, Moisés Augusto de Oliveira Borges, Bruno Lucas Pinheiro Lima, Vicente Pinheiro Lima

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2020
Typearticle
Languagept
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsAthletesTerm (time)Affect (linguistics)
DOInot available

Abstract

fetched live from OpenAlex

Objetivo: Determinar a prevalência da percepção de dor em pilotos de MotoCross amadores da Liga Esportiva de Motociclismo do Estado do Rio de Janeiro. Métodos: A amostra foi composta de 27 atletas do sexo masculino, com 40,63±5,15 de idade, participantes do Campeonato Estadual do Rio de Janeiro. Foi aplicado o teste de McGill a fim de mensurar as dores que os atletas sentem mediante a prática esportiva. Resultados: Os resultados mostram que na descrição de dor de dimensão SENSORIAL, para os descritores, (CÓLICA e a MORDIDA) foram obtidas as menores médias (0,19). O descritor (DOLORIDA) apresentou a maior média (0,97). Já na descrição de dor de dimensão AFETIVA, o descritor (CASTIGANTE) obteve a menor média (0,84). O descritor (AMEDRONTADA) apresentou a maior média. A média da EVA (Escala Visual Analógica) (1,07) e a média do desvio padrão (1,76). Conclusão: Os atletas não apresentaram uma diferença significante nos ní­veis de dor.

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)0.002
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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.034
GPT teacher head0.269
Teacher spread0.235 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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