Reliability, Standard Error, and Minimum Detectable Change of Clinical Pressure Pain Threshold Testing in People With and Without Acute Neck Pain
Bibliographic record
Abstract
STUDY DESIGN: Clinical measurement. OBJECTIVES: To evaluate the intrarater, interrater, and test-retest reliability of an accessible digital algometer, and to determine the minimum detectable change in normal healthy individuals and a clinical population with neck pain. BACKGROUND: Pressure pain threshold testing may be a valuable assessment and prognostic indicator for people with neck pain. To date, most of this research has been completed using algometers that are too resource intensive for routine clinical use. METHODS: Novice raters (physiotherapy students or clinical physiotherapists) were trained to perform algometry testing over 2 clinically relevant sites: the angle of the upper trapezius and the belly of the tibialis anterior. A convenience sample of normal healthy individuals and a clinical sample of people with neck pain were tested by 2 different raters (all participants) and on 2 different days (healthy participants only). Intraclass correlation coefficient (ICC), standard error of measurement, and minimum detectable change were calculated. RESULTS: A total of 60 healthy volunteers and 40 people with neck pain were recruited. Intrarater reliability was almost perfect (ICC = 0.94-0.97), interrater reliability was substantial to near perfect (ICC = 0.79-0.90), and test-retest reliability was substantial (ICC = 0.76-0.79). Smaller change was detectable in the trapezius compared to the tibialis anterior. CONCLUSIONS: This study provides evidence that novice raters can perform digital algometry with adequate reliability for research and clinical use in people with and without neck pain.
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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 | 0.032 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.000 |
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".