Measurement Error of a Simplified Protocol for Quantitative Sensory Tests in Chronic Pain Patients
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Large-scale application of Quantitative Sensory Tests (QST) is impaired by lacking standardized testing protocols. One unclear methodological aspect is the number of records needed to minimize measurement error. Traditionally, measurements are repeated 3 to 5 times, and their mean value is considered. When transferring QST to a clinical setting, reducing the number of records would be desirable to meet the time constraints encountered in a routine clinical environment and to reduce the testing burden to chronic pain patients. However, there might be a trade-off between measurement error and number of records. We determined the measurement error of a single versus the mean of 3 records of pressure pain detection threshold (PPDT), electrical pain detection threshold (EPDT), and nociceptive withdrawal reflex threshold (NWRT) in 429 chronic pain patients recruited in a routine clinical setting. METHODS: We calculated intraclass correlation coefficients and performed a Bland-Altman analysis. RESULTS: Intraclass correlation coefficients were all clearly greater than 0.75, and Bland-Altman analysis showed minute systematic errors with small point estimates and narrow 95% confidence intervals. Reducing the number of records from traditionally 3 to only 1 did not lead to relevant measurement error in PPDT, EPDT, or NWRT. CONCLUSIONS: This study contributes to a standardized QST protocol, and based on the minimal measurement error of 1 single record of PPDT, EPDT, and NWRT, we submit to reduce the testing burden. This would allow saving time, resources, and patient discomfort.
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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 | 0.105 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".