Novel Multidimensional Pain Assessment Tool Is a Feasible, Valid, and Enjoyable Approach to Communication of Pain Symptoms in Pediatric Sickle Cell Disease
Bibliographic record
Abstract
Background: Pain is the most common symptom of sickle cell disease (SCD) and is especially difficult to assess in the pediatric population. Children have varied developmental stages and cognitive abilities making the communication of this subjective experience hard to operationalize. Both the assessment of pain and validation of these assessments is challenging. No pediatric pain assessment tool has been deemed valid and reliable across all ages and types of pain. Commonly used pediatric tools include the Wong-Baker Faces scale, the numeric rating scale, and the visual analog scale (VAS), which combines the visuals of faces and numeric rating. These are unidimensional - only assessing the severity of pain, and in the case of faces, can cause confusion between pain severity and patient affect. In children with SCD, accurate pain assessment is crucial to current and lifelong management. “Painimation” is a technology-based pain assessment tool that incorporates conceptual animations, rather than numbers or words, to characterize pain. These animations are intended to help clinicians better understand pain from the patient's perspective. They are dynamic and transcend language. For example, animations are designed to communicate dull pulsating pain, sharp electric pain, or anything in between. “Painimation” does not rely on verbal communication skills, making it ideal for use in the pediatric population. “Painimation” has been validated in adults with SCD and is currently in implementation trials. Our study aims to determine the feasibility and validity of “Painimation” in pediatric SCD. Methods: This is a single site non-randomized cross-sectional feasibility and validity study. Participants were 10 - 21 years old with SCD who presented to clinic in baseline health. Participants completed the Painimation application, which consists of a numeric pain rating scale (1-10), a front and back 2-dimensional body image that can be shaded to indicate areas affected by pain, and eight abstract animations intended to represent different pain qualities (tingling, shooting, stabbing, throbbing, pounding, cramping, electrifying, and burning). The patient chose up to three animations and the intensity of each chosen animation was adjusted using a sliding bar without numeric labels. The animations were presented to the patient without labeling the intended quality. The patients then completed a survey consisting of questions regarding the usability of Painimation, clinical questions regarding their disease, Lansky Play-Performance Scale, and validated patient reported outcomes questions (PedsQL, Ped-PRO-CTCAE, PROMIS). Results: We enrolled 30 participants between April 1 and July 31, 2023, with recruitment ongoing. Five records were incomplete and were not analyzed at this time. The mean ratings when asked whether 1) Painimation was easy to use, 2) the patient enjoyed using Painimation, and 3) the patient would use Painimation to communicate pain with their provider were respectively 3.28±.74 (SD), 3.08±.95, and 3.12±1.17 (scale 0-4), indicating a generally favorable result for feasibility. The median VAS score was 5.10 (IQR = 0.25, 8.25). Pain was most reported in the back lower chest (60%), front stomach (48%), and front chest (44%). The stabbing animation was the most chosen at 56% followed by the cramping animation at 20%. Electrifying and Cramping animations had the highest agreement with their intended McGill descriptors at 40%. VAS scores and Painimation severity scores demonstrated excellent positive correlation (Pearson r=.74). Conclusion: Painimation is a feasible pain assessment tool in pediatric SCD with high user satisfaction. Severity scores on Painimation correlate strongly with VAS scores. The pain characteristic conveyed by each animation adds unique dimensions to this measure and will be further analyzed. Painimation is a novel pediatric pain assessment that holds the promise of greatly improving the communication of pain and transforming care in Pediatric SCD.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".