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Record W1418790031 · doi:10.3233/wor-2012-0476-2417

Low back pain disability and stay at work: contradiction or necessity?

2012· article· en· W1418790031 on OpenAlexaboutno aff
Rosimeire Simprini Padula, Rodrigo Luiz Carregaro, Bruna Evellyn Souza Melo, Cláudia Regina da Silva, Ana Beatriz Oliveira

Bibliographic record

VenueWork · 2012
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsLow back painPhysical therapyMedicineBack painIncidence (geometry)ComplaintPopulationMusculoskeletal painVisual analogue scalePain catastrophizingPhysical medicine and rehabilitationChronic painAlternative medicine

Abstract

fetched live from OpenAlex

The incidence of occupational diseases in the population is high and factors such as long working hours, poor posture, psychological and physical stress can contribute to its development. Among work-related musculoskeletal disorders, back pain has a high prevalence. The aim of the present study was to quantify and characterize pain complaints and to identify individuals with low back pain, in order to assess the degree of disability. Participated 226 employees of an institution of higher education. They answered a general questionnaire about location and quantification of pain complaints visual analog scale for pain and the Quebec Disability Questionnaire. Of all the workers, 69.60% had some type of musculoskeletal complaint; of those, 15.41% had low back pain. Considering workers who had back pain, 54.9% were female, 52.94% are under 30 years old and 43.14% between 1 and 5 years of work. As for the final score for the degree of disability, 41.17% had minimal disability and 37.25% moderate disability. The present study found large number of pain complaints and high prevalence of low back pain, resulting in individual's inability and difficulties in performing work activities.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.269
Teacher spread0.254 · 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 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

Citations5
Published2012
Admission routes1
Has abstractyes

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