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Posture and Loading in the Pathomechanics of Carpal Tunnel Syndrome: A Review

2016· review· en· W2744417448 on OpenAlexafffund
Nicolas Vignais, Justin Weresch, Peter J. Keir

Bibliographic record

VenueCritical Reviews in Biomedical Engineering · 2016
Typereview
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsMcMaster University
FundersAUTO21 Network of Centres of ExcellenceNatural Sciences and Engineering Research Council of Canada
KeywordsCarpal tunnel syndromeCarpal tunnelWristMedian nerveMedicineForearmHydrostatic pressurePhysical medicine and rehabilitationSurgery

Abstract

fetched live from OpenAlex

Carpal tunnel syndrome is a neuropathy of the median nerve at the wrist, and represents the most common peripheral neuropathy. It has long been an issue in the workplace because of healthcare costs and loss of productivity. The two main pathomechanisms of carpal tunnel syndrome include increased hydrostatic pressure within the carpal tunnel (carpal tunnel pressure) and contact stress (or impingement). As most cases of carpal tunnel syndrome in the workplace are labelled "idiopathic", a clear understanding of the physical parameters that may act as pathomechanisms is critical for its prevention. The aim of this review is to examine and quantify the influence of posture and loading factors on the increase of carpal tunnel pressure and median nerve contact stress. Forearm, wrist, and finger postures, as well as fingertip force have significant effects on carpal tunnel pressure. Contact stress on the median nerve is mainly a product of wrist posture and musculotendinous loading. Anatomical and musculoskeletal sources have been proposed to explain these effects. This critical review provides an improved understanding of pathomechanisms and etiology underlying carpal tunnel syndrome.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.033
GPT teacher head0.354
Teacher spread0.320 · 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 designNot applicable
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

Citations24
Published2016
Admission routes2
Has abstractyes

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