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Record W4310769219 · doi:10.1016/j.apergo.2022.103952

Physical and psychosocial work-related exposures and the occurrence of disorders of the elbow: A systematic review

2022· review· en· W4310769219 on OpenAlexfundno aff
Alessandro Chiarotto, Heike Gerger, Rogier M. van Rijn, Roy G. Elbers, Karen Søgaard, Erin M. Macri, Jennie A. Jackson, Alex Burdorf, Bart W. Koes

Bibliographic record

VenueApplied Ergonomics · 2022
Typereview
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsnot available
FundersWorkSafeBC
KeywordsEpicondylitisPsychosocialPsycINFOElbowPhysical therapyMedicineMEDLINEMeta-analysisPsychologyPsychiatrySurgeryInternal medicine

Abstract

fetched live from OpenAlex

This systematic review updates a previous systematic review on work-related physical and psychosocial risk factors for elbow disorders. Medline, Embase, Web of Science, Cochrane Central and PsycINFO were searched for studies on associations between work-related physical or psychosocial risk factors and the occurrence of elbow disorders. Two independent reviewers selected eligible studies and assessed risk of bias (RoB). Results of studies were synthesized narratively. We identified 17 new studies and lateral epicondylitis was the most studied disorder (13 studies). Five studies had a prospective cohort design, eight were cross-sectional and four were case-control. Only one study had no items rated as high RoB. Combined physical exposure indicators (e.g. physical exertion combined with elbow movement) were associated with the occurrence of lateral epicondylitis. No other consistent associations were observed for other physical and psychosocial exposures. These results prevent strong conclusions regarding associations between work-related exposures, and the occurrence of elbow disorders.

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)0.007
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.025
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.007
Bibliometrics0.0140.012
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.282
Teacher spread0.268 · 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 designSystematic review
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
Published2022
Admission routes1
Has abstractyes

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