Reliability and minimal detectable change of dynamic temporal summation and conditioned pain modulation using a single experimental paradigm
Bibliographic record
Abstract
BACKGROUND: Quantitative sensory tests (QST) are frequently used to explore alterations in somatosensory systems. Static and dynamic QST like pain threshold and temporal summation (TS) and conditioned pain modulation (CPM) are commonly used to evaluate excitatory and inhibitory mechanisms involved in pain processing. The aim of the present study was to document the reliability and the minimal detectable change (MDC) of these dynamic QST measurements using a standardized experimental paradigm. MATERIAL AND METHODS: Forty-six (46) pain-free participants took part in 2 identical sessions to collect TS and CPM outcomes. Mechanical (pressure pain threshold [PPT]) and thermal (constant 2-minute heat pain stimulation [HPS]) nociceptive stimuli were applied as test stimuli, before and after a cold-water bath (conditioning stimulus). TS was interpreted as the change in pain perception scores during HPS. CPM were determined by calculating the difference in pain perception between pre- and post- water bath for both PPT and HPS. Relative and absolute reliability were analyzed with intra-class correlation coefficient (ICC2, k), standard error of the measurements (SEMeas) and MDC. RESULTS: Results revealed a good to excellent relative reliability for static QST (ICC ≥ 0.73). For TS, a poor to moderate relative reliability depending on the calculation methods (ICC = 0.25 ≤ ICC ≤ 0.59), and a poor relative reliability for CPM (ICC = 0.16 ≤ ICC ≤ 0.37), both when measured with mechanical stimulation (PPT) and thermal stimulation (HPS). Absolute reliability varied from 0.73 to 7.74 for static QST, 11 to 22 points for TS and corresponded to 11.42 points and 1.56 points for thermal and mechanical-induced CPM, respectively. MDC analyses revealed that a change of 1.58 to 21.46 point for static QST, 31 to 52 points for TS and 4 to 31 points for CPM is necessary to be interpreted as a real change. CONCLUSION: Our approach seems well-suited to clinical use. Although our method shows equivalent relative and absolute reliability compared to other protocols, we found that the reliability of endogenous pain modulation mechanisms is vulnerable, probably due to its dynamic nature.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".