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Record W1513757334 · doi:10.3233/wor-2010-0990

Perspectives on prevention, assessment, and rehabilitation of low back pain in WORK

2010· review· en· W1513757334 on OpenAlexaff
Michael J. Ravenek, Mikelle Bryson-Campbell, Lynn Shaw, Ian Hughes

Bibliographic record

VenueWork · 2010
Typereview
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsRehabilitationWork (physics)RealmRelevance (law)Low back painKnowledge baseMedicineAlternative medicinePhysical therapyPolitical scienceEngineeringComputer sciencePathology

Abstract

fetched live from OpenAlex

INTRODUCTION: The aim of this review was to describe the low back pain (LBP) knowledge base developed in WORK and to discuss its relevance to current perspectives in the broader literature on LBP and employment. METHOD: A scoping review of the literature in WORK on LBP and employment was conducted using published articles from 1990-2009. Articles were organized into geographical regions and summarized for contributions to the domains of WORK: prevention, assessment, and rehabilitation. Methodological accordance of the articles was also assessed. RESULTS: Fifty articles were extracted and organized into contributions from authors within North America (n=34) and outside North America (n=16). In total there were 26 prevention, 7 assessment, and 12 rehabilitation articles in this review. Five articles were also classified as 'understanding' articles. More than half of the articles retrieved employed quantitative methodology. CONCLUSIONS: WORK has contributed a broad realm of publications to the knowledge base on LBP and employment. Two thirds of the articles were contributed from authors within North America, with a greater emphasis on prevention. This article highlights the similarities and differences in the international knowledge base in the management of LBP in WORK. Future directions for research are elaborated drawing on current perspectives of two experts on the management of LBP.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.949
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.363
Teacher spread0.345 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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

Citations11
Published2010
Admission routes1
Has abstractyes

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