DEVELOPMENT OF A DIGITAL PAIN MAPPING TOOL USING ICONOGRAPHY FOR THE ASSESSMENT OF SENSORY PAIN
Why this work is in the frame
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Bibliographic record
Abstract
The overall theme of this thesis is the study of sensory pain assessment and describes how digital pain mapping using standardized iconography can be used to help portray and understand the sensory pain experience. The research presented in this thesis is focused on the design, development, and use of a web-based sensory pain assessment tool for individuals with chronic pain called the Pain-QuILT. “QuILT” is an acronym describing the different parameters that are captured by the tool: pain quality, intensity, and location in a digital format that can be tracked over time. The central hypothesis guiding this work is that users of pain assessment tools will tend to favour a digital icon-based sensory pain mapping tool (‘PainQuILT’) over currently available sensory pain assessment tools. “Pain assessment tool” has been operationally defined as a standardized method for capturing information about an individual’s sensory pain experience. In this context, “users” include both individuals experiencing chronic pain and healthcare providers who seek to assess and understand pain. Research to date has focused on phased evaluation of the Pain-QuILT in the context of clinical sensory pain assessment for two distinct user groups: adolescents (aged 12 to 18 years) and adults (aged 19 years and older) with chronic pain. Each stage of research has generated and been informed by user feedback, leading to iterative improvements in tool functionality. Thus, as a whole, this body of work represents an evolving effort to improve the clinical assessment of sensory pain using the approach of icon-based pain mapping in a digital and visual format. Through the collective research presented in this thesis, we have affirmed that digital pain mapping using iconography is a viable solution to the clinical challenge of sensory pain assessment in adolescents and adults with chronic pain.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it