Pain-QuILT: Clinical Feasibility of a Web-Based Visual Pain Assessment Tool in Adults With Chronic Pain
Bibliographic record
Abstract
BACKGROUND: Chronic pain is a prevalent and debilitating problem. Accurate and timely pain assessment is critical to pain management. In particular, pain needs to be consistently tracked over time in order to gauge the effectiveness of different treatments. In current clinical practice, paper-based questionnaires are the norm for pain assessment. However, these methods are not conducive to capturing or tracking the complex sensations of chronic pain. Pain-QuILT (previously called the Iconic Pain Assessment Tool) is a Web-based tool for the visual self-report and tracking of pain (quality, intensity, location, tracker) in the form of time-stamped records. It has been iteratively developed and evaluated in adolescents and adults with chronic pain, including usability testing and content validation. Clinical feasibility is an important stepping-stone toward widespread implementation of a new tool. Our group has demonstrated Pain-QuILT clinical feasibility in the context of a pediatric chronic pain clinic. We sought to extend these findings by evaluating Pain-QuILT clinical feasibility from the perspective of adults with chronic pain, in comparison with standard paper-based methods (McGill Pain Questionnaire [MPQ] and Brief Pain Inventory [BPI]). OBJECTIVE: The goal of our study was to assess Pain-QuILT for (1) ease of use, (2) time for completion, (3) patient preferences, and (4) to explore the patterns of self-reported pain across the Pain-QuILT, MPQ, and BPI. METHODS: Participants were recruited during a scheduled follow-up visit at a hospital-affiliated pain management and physical rehabilitation clinic in southwestern Ontario. Participants self-reported their current pain using the Pain-QuILT, MPQ, and BPI (randomized order). A semistructured interview format was used to capture participant preferences for pain self-report. RESULTS: The sample consisted of 50 adults (54% female, 27/50) with a mean age of 50 years. Pain-QuILT was rated as significantly easier to use than both the MPQ and BPI (P<.01) and was also associated with the fewest difficulties in completion. On average, the time to complete each tool was less than 5 minutes. A majority of participants (58%, 29/50) preferred Pain-QuILT for reporting their pain over alternate methods (16%, 8/50 for MPQ; 14%, 7/50 for BPI; 12%, 6/50 for "other"). The most commonly chosen pain descriptors on MPQ were matched with Pain-QuILT across 91% of categories. There was a moderate-to-high correlation between Pain-QuILT and BPI scores for pain intensity (r=.70, P<.01). CONCLUSIONS: The results of this clinical feasibility study in adults with chronic pain are consistent with our previously published pediatric findings. Specifically, data indicate that Pain-QuILT is (1) easy to use, (2) quick to complete, (3) preferred by a majority of patients, and (4) correlated as expected with validated pain measures. As a digital, patient-friendly method of assessing and tracking pain, we conclude that Pain-QuILT has potential to add significant value as one standard component of chronic pain management.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".