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Record W2609072364

Prevalência de dor lombar em praticantes de musculação

2017· article· pt· W2609072364 on OpenAlexaboutno aff
Rafaelli F. Carniel de Souza, Altair Argentino Pereira Júnior

Bibliographic record

VenueRevista da UNIFEBE · 2017
Typearticle
Languagept
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsPhysical therapyMedicineGynecologyPsychologyPhysical medicine and rehabilitation
DOInot available

Abstract

fetched live from OpenAlex

O objetivo da pesquisa foi verificar a incidencia de dor lombar em praticantes de musculacao. A amostra foi composta por 40 individuos de ambos os generos, com idade compreendida entre 16 e 50 anos, praticantes de musculacao.  Como instrumento utilizou-se o questionario Quebec Pain Disability Scale para Lombalgia e uma ficha de avaliacao individual. Os dados foram analisados pela estatistica descritiva. Entre os resultados observou-se que vinte e sete pesquisados apresentaram dor lombar. As principais atividades que ocasionam a dor foram ficar em pe por 20 – 30 minutos, sentar em uma cadeira por varias horas, caminhar varios quilometros, levantar e carregar uma mala pesada. Conclui-se que 67% dos alunos praticantes de musculacao que foram entrevistados sentem dor lombar e que apesar do desconforto continuam praticando o exercicio fisico sem tratamento medico ou cuidados com determinados exercicios que possuem sobrecarga axial. O exercicio de agachamento com barra foi citado como o causador de maior desconforto na regiao lombar.

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)0.002
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.021
GPT teacher head0.329
Teacher spread0.308 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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