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Record W4311822101 · doi:10.23749/mdl.v113i6.13652

A method for the risk assessment of biomechanical overload in hospital physiotherapists

2022· article· en· W4311822101 on OpenAlexaff
Emma Sala, Andrea Bisioli, Pietro Ponzoni, Alessandro Bellis, Emilio Paraggio, Giuseppe De Palma

Bibliographic record

Venue˜La œMedicina del lavoro · 2022
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsMedicineRisk assessmentPhysical medicine and rehabilitationPhysical therapyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: In physiotherapists, biomechanical overload risk assessment (RA) is particularly complex due to the tasks' variability. The present study aims to propose a new methodology, named Whole Body RA Biomechanical Overload (WB-RAMBO), to assess the risk in the activities performed by physiotherapists. METHODS: Each type of intervention was broken down into elementary operations. The risk factors (force, repetitiveness, and incongruous postures) were recorded and evaluated for each of these. For each task, the risk level was obtained by integrating the results of multiple ergonomic methods among those proposed by the international literature. To verify and validate the obtained results, we reviewed the medical records of health surveillance carried out on physiotherapists. RESULTS: From the ergonomic point of view, RA shows a situation of acceptability. The observed slight dysergonomies are diluted in the work shift and allow an optimal functional recovery of the musculoskeletal system. CONCLUSIONS: This method proposes a RA for each operation performed. A work plan subjected to such a peculiar RA can be redesigned and adapted to the company's and the hypersusceptible worker's organizational needs.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.008
GPT teacher head0.347
Teacher spread0.339 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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Same venue˜La œMedicina del lavoroSame topicMusculoskeletal pain and rehabilitationFrench-language works237,207