DEVELOPMENT OF A DIGITAL PAIN MAPPING TOOL USING ICONOGRAPHY FOR THE ASSESSMENT OF SENSORY PAIN
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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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".