35th Annual Scientific Meeting of the Canadian Pain Society: Abstracts
Bibliographic record
Abstract
The brain holds maps of the body and the space around it.These maps subserve the regulation and protection of our body, and the space around it, both physiological and psychologically.That is, these maps are integrated with motor, sensory, and homeostatic functions as well as with the feelings we have of our body -its perceived size, location and temperature; that we own it and have agency over it.A growing body of literature suggests that many of these maps are disrupted in people with chronic pain.Our conventional understanding of how these maps would predict that their disruption simply reflects disrupted peripheral input-a purely 'bottom-up' phenomenon.However, recent experiments clearly reveal 'topdown' effects as well, which implies that disrupted cortical maps of space and body might contribute to the development or maintenance of chronic pain.That would raise the tantalizing possibility that these disrupted maps might be viable targets for treatment.Indeed, several treatments have been developed and preliminary results appear promising.This lecture will discuss the current state of research in this area, from the studies that underpin the idea of 'training the brain' for chronic pain, to the current state of evidence for their effectiveness.Learning Objectives: 1.To understand the idea of cortical maps of the body and space around it.2. To understand the evidence that these cortical maps are disrupted in people with chronic pain.3. To understand the current evidence concerning treatments that aim to correct these disruptions as a way of treating 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.194 | 0.075 |
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".