Skin-fold Thickness and Pressure Pain Threshold in the Upper Arm and Torso of Women
Bibliographic record
Abstract
The purpose of this study was to evaluate the relationship between skin-fold thickness and pressure pain threshold (PPT) values in the upper arm and torso in young women. METHODS: Nineteen college-aged women (20-39 yrs) with no underlying musculoskeletal problems participated in this study that evaluated the PPT values and skin fold values of eight different locations in the upper extremity and torso. RESULTS: PPT values varied from a low of 150.09 ± 77.15 kPa at location 1 to a high of 246.53 ± 78.01 kPa at location 4. An ANOVA was performed on the PPT values obtained at each of the eight locations. Post-hoc analysis showed a significant difference (P<0.05) between the PPT values obtained at the different locations. Skin-fold thickness varied from a low of 8.94 mm ± 4.24 at location 2 to a high of μ=17.38mm ± 6.05 at location 3. An ANOVA was also performed on the skin-fold values obtained at each of the eight locations, Post-hoc analysis showed a significant difference (P<0.05) between the skin-fold values obtained at the eight locations. A Pearson correlation analysis showed that there was no relationship (r2 = 0.05) between the PPT values and the skin-fold thickness obtained at the eight different locations. Locations on subjects with increased skin-fold thickness did not lead to higher PPT values. Locations on subjects with lower skin-fold thickness did not lead to lower PPT values. CONCLUSION: Higher skin-fold values help individuals to tolerate colder climates. Studies have clearly shown that in cold environments, individuals with increased level of adipose tissue are better suited to reducing the amount of heat loss in the body, compared to individuals with lower levels of body fat. This insulation effect does not appear to be factor when tolerating mechanical pressure; if it did, we would expect to see higher pressure pain threshold values closely correlated to higher skin fold measures.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".