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Record W1975696285 · doi:10.1177/0954411914562871

Characterization of a pressure measuring system for the evaluation of medical devices

2014· article· en· W1975696285 on OpenAlexaboutno aff
Rébecca Bonnaire, M. Verhaeghe, Jérôme Molimard, P. Calmels, Reynald Convert

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine · 2014
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsRepeatabilitySuperposition principleCalibrationLinearitySystem of measurementReproducibilityMetrologyHysteresisResistive touchscreenPressure measurementMeasurement uncertaintyMaterials scienceAcousticsOpticsElectrical engineeringMathematicsMechanical engineeringEngineeringPhysicsStatisticsMathematical analysis

Abstract

fetched live from OpenAlex

The purpose of this study is to evaluate the possible use of four "FSA" thin and flexible resistive pressure mapping systems, designed by Vista Medical (Winnipeg, Manitoba, Canada), for the measurement of interface pressure exerted by lumbar belts onto the trunk. These sensors were originally designed for the measurement of low pressure applied by medical devices on the skin. Two types of tests were performed: standard metrology tests such as linearity, hysteresis, repeatability, reproducibility and drift, and specific tests for this application such as curvature, surface condition and mapping system superposition. The linear regression coefficient is between 0.86 and 0.98; hysteresis is between 6.29% and 9.41%. Measurements are repeatable. The location, time and operator, measurement surface condition and mapping system superposition have a statistically significant influence on the results. A stable measure is verified over the period defined in the calibration procedure, but unacceptable drift is observed afterward. The measurement stays suitable on a curved surface for an applied pressure above 50 mmHg. To conclude, the sensor has acceptable linearity, hysteresis and repeatability. Calibration must be adapted to avoid drift. Moreover, when comparing different measurements with this sensor, the location, the time, the operator and the measurement surface condition should not change; the mapping system must not be superimposed.

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)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
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 head0.033
GPT teacher head0.292
Teacher spread0.259 · 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 designSimulation or modeling
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

Citations12
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in MedicineSame topicErgonomics and Musculoskeletal DisordersFrench-language works237,207