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Record W4394175037 · doi:10.6084/m9.figshare.14325806

Low back pain among bodybuilding professors of the West zone of the city of Rio de Janeiro

2021· dataset· en· W4394175037 on OpenAlexaboutno aff
Jurandir Baptista da Silva, Rodrigo Gomes de Souza Vale, Flavio Da Silva, Adeilson Chagas, G. J. de Moraes, Vicente Pinheiro Lima

Bibliographic record

VenueFigshare · 2021
Typedataset
Languageen
FieldHealth Professions
TopicOccupational health in dentistry
Canadian institutionsnot available
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

ABSTRACT BACKGROUND AND OBJECTIVES: Low back pain is one of the most common musculoskeletal symptoms in industrialized societies, according to the World Health Organization. This study aimed at investigating the prevalence of low back pain among bodybuilding professors of fitness centers of the city of Rio de Janeiro and at observing correlations between age, working time, working hours and low back pain intensity. METHODS: The adapted questionnaire of the Quebec Pain Disability Scale was applied to 50 physical education professors of both genders (age = 31.86±6.86 years) working with bodybuilding in fitness centers, with minimum weekly working hours of 12h, and at least three years acting in the area. This was a survey-type descriptive cross-sectional study. RESULTS: From 50 interviewed professors, 62% have stated not feeling any type of lumbar discomfort, while just 38% have stated feeling some type of pain. From these, 20% have stated feeling daily pain, 6% weekly and 12% have reported monthly pain. About pain intensity in its worst moment, 14% have stated it is mild, 20% moderate and just 6% have reported severe pain. There has been positive and significant correlation (p<0.05) between age and working time and between working time and low back pain intensity. CONCLUSION: Low back pain prevalence was not high among interviewed professionals. Results show that older individuals working for a longer time are those with more severe low back pain.

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)0.001
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.426
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1430.000

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 head0.121
GPT teacher head0.431
Teacher spread0.310 · 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 designNot applicable
Domainnot available
GenreDataset

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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