The use of abstract animations and a graphical body image for assessing pain outcomes among adults with sickle cell disease
Bibliographic record
Abstract
Painimation, a novel digital pain assessment tool, allows patients to communicate their pain quality, intensity, and location using abstract animations (painimations) and a paintable body image. This study determined the construct validity of painimations and body image measures by testing correlations with validated pain outcomes in adults with sickle cell disease (SCD). Analyses used baseline data from a multisite randomized trial of 359 adults with SCD and chronic pain. Participants completed questionnaires on demographics, pain severity, frequency and interference, catastrophizing, opioid use, mood and quality of life, plus the Painimation app. Participants were categorized by selected painimations, and were split into groups based on the proportion of painted body image. Potential confounding was evaluated by age, gender, race, education, disability, site, depression, and anxiety. The 'shooting' painimation was strongly associated with daily pain intensity, pain interference, frequency, and severity. 'Electrifying' was associated with daily pain and opioid misuse, while greater body area in pain correlated with worse outcomes across all pain measures. Both painimations and body image measures correlated with validated pain outcomes, quality of life and mental health measures. This demonstrates animations and body image data can assess SCD pain severity, potentially with more accuracy than a 0-10 scale. Future research will explore whether Painimation can differentiate biological and psychosocial pain components. PERSPECTIVE: This article presents the preliminary construct validity of Painimation in SCD by examining the associations of "painimations" and body area image data with daily e-diary and traditional self-report pain outcomes.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".