The effect of probe to skin contact force on Cutometer MPA 580 measurements
Bibliographic record
Abstract
The purpose of this study is to test the hypothesis that increasing the force applied on the skin by the Cutometer MP580 probe will result in a decrease in the skin elasticity measures. Specifically, this study assessed the probe intrinsic weight plus the addition of a light mass (10 g and 20 g), a moderate mass (50 g and 100 g) and a high mass (200 g and 500 g) on skin elasticity measures. Primary outcome measures Uv, Ur, Uf, Ue and Ua, along with calculated measures Uv/Uf, Ua/Uf and Ur/Uf were assessed under each loading condition. A general linear model ANOVA with repeated measures was used to assess for differences in each outcome measure between each loading condition. Thirty-two patients were enrolled and all completed the testing. For all primary variables except Uv (p < 0.001), there was no statistically significant effect of adding a light mass to the probe. There was a significant effect of the addition of a moderate and heavy mass for all variables (p < 0.005) except Ue/Uf. These results suggest that the addition of a low mass results in no significant effect on outcome measures. However, if moderate-to-heavy additional force is applied to the probe, the outcome measures are significantly altered. Of all the variables, Ue/Uf appears to be influenced the least by alterations in force. Users should ensure light contact is made between the skin and probe during testing to avoid a false alteration in outcome measures of skin elasticity.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".