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Record W4390743632 · doi:10.1016/j.apergo.2023.104211

Physical and psychosocial work-related exposures and the incidence of carpal tunnel syndrome: A systematic review of prospective studies

2024· review· en· W4390743632 on OpenAlexfundno aff
Heike Gerger, Erin M. Macri, Jennie A. Jackson, Roy G. Elbers, Rogier van Rijn, Karen Søgaard, Alex Burdorf, Bart W. Koes, Alessandro Chiarotto

Bibliographic record

VenueApplied Ergonomics · 2024
Typereview
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsnot available
FundersWorkSafeBC
KeywordsPsychosocialMedicineCarpal tunnel syndromeIncidence (geometry)Cohort studyProspective cohort studyCohortPhysical therapyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

This systematic review summarizes the evidence on associations between physical and psychosocial work-related exposures and the development of carpal tunnel syndrome (CTS). Relevant databases were searched up to January 2020 for cohort studies reporting associations between work-related physical or psychosocial risk factors and the incidence of CTS. Two independent reviewers selected eligible studies, extracted relevant data, and assessed risk of bias (RoB). We identified fourteen articles for inclusion which reported data from nine cohort studies. Eight reported associations between physical exposure and the incidence of CTS and five reported associations between psychosocial exposures and the incidence of CTS. Quality items were generally rated as unclear or low RoB. Work-related physical exposure factors including high levels of repetition, velocity, and a combination of multiple physical exposures were associated with an increased risk of developing CTS. No other consistent associations were observed for physical or psychosocial exposures at work and CTS incidence.

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)0.006
metaresearch head score (Gemma)0.028
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 score0.012
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.028
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.008
Bibliometrics0.0120.014
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.020
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 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

Citations18
Published2024
Admission routes1
Has abstractyes

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