Validation of Plantar Pressure Measurements for a Novel in-Shoe Plantar Sensory Replacement Unit
Bibliographic record
Abstract
BACKGROUND: Research concerning prevention of diabetic foot complications is critical. A novel in-shoe plantar sensory replacement unit (PSRU) has been developed that provides alert-based feedback derived from analyzing plantar pressure threshold measurements in real time. The purpose of this study was to compare the PSRU device to a gold standard pressure-sensing device (GS-PSD) to determine the correlation between concurrent measures of plantar pressure during walking. METHODS: The PSRU had an array of eight sensors with a range of 10-75 mm Hg and collected data at 4 Hz, whereas the GS-PSD had 99 sensors with a range of 1-112 mm Hg and collected data at 100 Hz. Based on an a priori power analysis, data were collected from 10 participants (3 female, 7 male) while walking over ground in both devices. The primary variable of interest was the number of data points recorded that were greater than 32 mm Hg (capillary arterial pressure--the minimum pressure reported to cause pressure ulcers) for each of the eight PSRU sensors and corresponding average recordings from the GS-PSD sensor clusters. Intraclass correlation coefficient (2,1) was used to compare data between the two devices. RESULTS: Compared with the GS-PSD, we found good-to-very-good correlations (r-value range 0.67-0.86; p-value range 0.01-0.05) for six of the PSRU's eight sensors and poor correlation for only two sensors (r = 0.41, p = .15; r = 0.38, p = .18) when measuring the number of data points recorded that were greater than 32 mm Hg. CONCLUSIONS: Based on the results of the present study, we conclude the PSRU provides analogous data when compared with a GS-PSD.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".