The reliability of pressure pain threshold in individuals with low back or neck pain: a systematic review
Bibliographic record
Abstract
Background and Objective: Low-back and neck pain affect a great number of individuals worldwide. The pressure pain threshold has the potential to be a useful quantitative measure of mechanical pain in a clinical setting, if it proves to be reliable in this population. The objectives of this systematic review are to: (1) analyze the literature evaluating the reliability of pressure pain threshold (PPT) measurements in the assessment of neck and low-back pain, (2) summarize the evidence from these studies, and (3) characterize the limitations of PPT measurement. Databases and Data Treatment: Relevant literature from PubMed and the Web of Science electronic databases were screened in a 3-step process according to inclusion/exclusion criteria. Relevant studies were assessed for risk of bias using the Quality Appraisal of Reliability Studies (QAREL) tool, and results of all studies were summarized and tabulated. Results: = 200) were consistently reported to be good to excellent (ICC 0.75-0.99 and ICC 0.81-0.90, respectively). Studies were also found to have significant variation in PPT measurement procedures. Conclusions: Though intra- and inter-rater reliability was found to be high in all studies, the variation in PPT measurement protocols could affect validity and absolute reliability. As such, it is recommended that standard guidelines be developed for clinical use.
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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 | 0.015 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".