Quantifying Pain Location and Intensity with Multimodal Pain Body Diagrams
Bibliographic record
Abstract
To quantify an individual's subjective pain severity, standardized pain rating scales such as the numeric rating scale (NRS), visual analog scale (VAS), or McGill pain questionnaire (MPQ) are commonly used to assess pain on a numerical scale. However, these scales are often biased and fail to capture the complexity of pain experiences. In contrast, clinical practice often requires patients to report areas of pain by drawing on a body diagram, which is an effective but qualitative tool. The method presented here extracts quantifiable metrics from pain body diagrams (PBDs) which are validated against the NRS, VAS, and MPQ pain scales. By using a novel pressure-hue transformation on a digital tablet, different drawing pressures applied with a digital stylus can be represented as different hues on a PBD. This produces a visually intuitive diagram of hues ranging from green to blue to red, representing mild to moderate to most painful regions, respectively. To quantify each PBD, novel pain metrics were defined: (1) PBD mean intensity, which equals the sum of each pixel's hue value divided by the number of colored pixels, (2) PBD coverage, which equals the number of colored pixels divided by the total number of pixels on the body, and (3) PBD sum intensity, which equals the sum of all pixels' hue values. Using correlation and information theory analyses, these PBD metrics were shown to have high concordance with standardized pain metrics, including NRS, VAS and MPQ. In conclusion, PBDs can provide novel spatial and quantitative information that can be repeatedly measured and tracked over time to comprehensively characterize a participant's pain experience.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".