Reliability of an algometry device for pain pressure threshold evaluation in athletes: A pilot study
Bibliographic record
Abstract
The pain pressure threshold is a metric used in both rehabilitation and diagnostic settings for various applications. Typically, this threshold is determined using a handheld dynamometer or an analog/digital algometer, which measures sensitivity to the onset of pain. However, human administration of pressure is prone to errors due to variations in the rate of force application, angle of application, and human responsiveness. In this study, we aimed to develop a prototype device that automates pressure applications. This device can apply pressure at different rates and angles, allowing participants to cease pressure application autonomously. The device was tested on the rectus femoris of 41 participants (30 males and 11 females), and the rate of force application was set to 0.05 m/s at a 90-degree angle. The device's reliability was assessed using intra-class correlation (ICC), and the results demonstrated high reliability (ICC = 0.91) for both males and females with a standard error of measurement of 4.34. Statistical analysis revealed that males exhibited a significantly higher pain pressure threshold than females. This study confirms the reliability of the prototype device in measuring pain pressure threshold with minimal examiner intervention. Additionally, the higher pain pressure threshold of males, suggests lower sensitivity to pressure pain compared to females.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".