Beyond numbers: integrating qualitative analysis into quantitative sensory testing for neuropathic pain
Bibliographic record
Abstract
This article investigates the benefits of adopting qualitative and quantitative sensory testing (QQST) in sensory assessment, with a focus on understanding neuropathic pain. The innovative QQST method combines participant qualitative experiences with quantitative psychophysical measurements, offering a more varied interpretation of sensory abnormalities and normal sensory function. This article also explores the steps for the optimization of the method by identifying qualitative signs of sensory abnormalities and standardizing data collection. By leveraging the inherent subjectivity in the test design and participant responses, the QQST method contributes to a more holistic exploration of both normal and abnormal sensory experiences. This article positions the QQST approach as a foundational element within the Sensory Evaluation Network, uniting international experts to harmonize qualitative and quantitative sensory evaluation methods.
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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 | 0.154 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".