Evaluation of the Iconic Pain Assessment Tool by a Heterogeneous Group of People in Pain
Bibliographic record
Abstract
The Iconic Pain Assessment Tool (IPAT) is a novel web-based instrument for the self-report of pain quality, intensity and location in the form of a permanent diary. Originally designed for people with central poststroke pain, the tool is being adapted for a larger, more diverse patient population. The present study aimed to collect evaluative feedback on the IPAT from a heterogeneous sample of individuals with chronic pain. The specific study aims were to evaluate participant comfort with the tool including enjoyment, ease of use and comfort with the electronic medium; to assess perceived value of the tool for communicating pain quality, intensity and location; to gauge participant intent to share their pain diaries with others and use the tool on a regular basis to track their pain over time; to assess the perceived descriptiveness of current IPAT icons and the numerical rating scale; and to identify strengths and weaknesses of the tool to refine the existing prototype. Written and verbal feedback from individuals with a variety of chronic pain conditions (n=23) were collected in the context of these objectives. Overall, the IPAT was positively endorsed by this heterogeneous sample of people in pain. The authors concluded that the IPAT is a user-friendly instrument that has the potential to help people express, document and share their personal experience with chronic pain.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".