Characterization of a pressure measuring system for the evaluation of medical devices
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".