Test–retest and interobserver reliability of quantitative sensory testing according to the protocol of the German Research Network on Neuropathic Pain (DFNS): A multi-centre study
Bibliographic record
Abstract
Quantitative sensory testing (QST) is an instrument to assess positive and negative sensory signs, helping to identify mechanisms underlying pathologic pain conditions. In this study, we evaluated the test-retest reliability (TR-R) and the interobserver reliability (IO-R) of QST in patients with sensory disturbances of different etiologies. In 4 centres, 60 patients (37 male and 23 female, 56.4±1.9years) with lesions or diseases of the somatosensory system were included. QST comprised 13 parameters including detection and pain thresholds for thermal and mechanical stimuli. QST was performed in the clinically most affected test area and a less or unaffected control area in a morning and an afternoon session on 2 consecutive days by examiner pairs (4 QSTs/patient). For both, TR-R and IO-R, there were high correlations (r=0.80-0.93) at the affected test area, except for wind-up ratio (TR-R: r=0.67; IO-R: r=0.56) and paradoxical heat sensations (TR-R: r=0.35; IO-R: r=0.44). Mean IO-R (r=0.83, 31% unexplained variance) was slightly lower than TR-R (r=0.86, 26% unexplained variance, P<.05); the difference in variance amounted to 5%. There were no differences between study centres. In a subgroup with an unaffected control area (n=43), reliabilities were significantly better in the test area (TR-R: r=0.86; IO-R: r=0.83) than in the control area (TR-R: r=0.79; IO-R: r=0.71, each P<.01), suggesting that disease-related systematic variance enhances reliability of QST. We conclude that standardized QST performed by trained examiners is a valuable diagnostic instrument with good test-retest and interobserver reliability within 2days. With standardized training, observer bias is much lower than random variance. Quantitative sensory testing performed by trained examiners is a valuable diagnostic instrument with good interobserver and test-retest reliability for use in patients with sensory disturbances of different etiologies to help identify mechanisms of neuropathic and non-neuropathic 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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".