How to think about pain with the whole person in mind
Bibliographic record
Abstract
Too often, pain is reduced to a simple symptom of illness or injury – a puzzle piece to fit into the differential diagnostic jigsaw. Pain reports that fit the emerging pathoanatomical picture are validated and treated accordingly. But many reports don’t fit this picture, and the widespread stigma associated with persistent pain is most commonly directed toward these individuals, whose symptoms aren’t well explained by known pain mechanisms. A root problem is not seeing the person in pain or the suffering they experience. This presentation aims to help participants develop a more comprehensive perspective on pain that better integrates its complexities within clinical practice. Participants will be introduced to the Multi-modal Assessment model of Pain (MAP; Wideman et al, Clinical Journal of Pain 2019; 35(3): 212). MAP offers a novel framework to understand the fundamentally subjective natures of pain and suffering and how they can be best addressed within clinical practice. MAP aims to help clinicians view pain, first and foremost, as an experience (like sadness), which may or may not correspond to specific pathology or diagnostic criteria (like clinical depression). MAP aims to facilitate a more compassionate approach to pain management by providing a rationale for why all reported pain should be validated, even when poorly understood. Viewing pain in this manner helps highlight the central importance of listening to patients’ narrative reports, trying to understand the meaning and context for their experiences of pain and using this understanding to help alleviate suffering.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 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".