MétaCan
Menu
Back to cohort
Record W4319937640 · doi:10.3233/wor-220185

Design and ergonomic evaluation of a new adapted endoscope holder to reduce muscle pressure and discomfort during endoscopy

2023· article· en· W4319937640 on OpenAlexaff
Mohammad Sadegh Ghasemi, Morteza Siadat, Bessam Abdulrazak, Naser Dehghan, Gabriel Ibarra-Mejía, Bijan Forogh

Bibliographic record

VenueWork · 2023
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsEndoscopeEndoscopyMedicineComputer scienceBiomedical engineeringSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Laparoscopic surgeons who regularly perform endoscopy are more likely to develop musculoskeletal disorders than other internal medicine specialists, a difference that attributed to repetitive movements, poor postures, and sub-optimal equipment design. OBJECTIVE: This study aimed to design, build, and evaluate an endoscope holder for reducing the static load applied by the weight of the endoscope, in order to reduce musculoskeletal disorders risk factors in the surgeon's hand, shoulder and back issues regions. METHODS: A new endoscope holder was designed according to ergonomic design principles. The designed holder was evaluated by surface electromyography (sEMG) and discomfort assessment including 15 laparoscopic surgeons. The results were analyzed with centrality statistics and compared with the independent t-test using SPSS version 22. RESULTS: The evaluation of the new endoscope holder shows a statistical significant decrease in the average electrical activity of biceps brachii, triceps brachii, lateral deltoid, T9 Thoracic erector spinae, L4 Lumbar erector spinae, and external oblique after using the holder (p < 0.05). CONCLUSION: The results shows that using the new endoscope holder is associated with a lower level of discomfort, as well as a lower induced muscle activity. The results also highlight the need to upgrade the holder to offer rotability in all directions (perpendicular to the ground) which will be included in the next design.

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)0.000
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 score0.885
Threshold uncertainty score0.258

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.091
GPT teacher head0.342
Teacher spread0.251 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueWorkSame topicSurgical Simulation and TrainingFrench-language works237,207