ELECTIVE REPORT: A Hospital-Based Multidisciplinary Approach To Chronic Pain Management
Bibliographic record
Abstract
The Alan Edwards Pain Management Unit at the Montreal General Hospital is a bilingual hospital-based multidisciplinary chronic pain treatment facility. Its members are practitioners in the disciplines of anaesthesiology, physiatry, rheumatology, nursing, psychology, physiotherapy, family practice, psychiatry, palliative medicine and others. The patients who present typically have chronic, often unrelenting, pain for years which progresses to a complex condition that drastically changes their physical, social, mental and emotional level of functioning. The team works together to synthesize both medical and relevant psychological issues to develop individual treatment programs using multiple treatment modalities including pharmacological management, physiotherapy, psychological techniques, clinic based procedures and operating room based interventions. The unit is also dedicated to academic endeavors including conducting pain research, hosting instructional rounds and the teaching of fellows, residents and medical students. As a student welcomed there on a 2 week elective, I had the opportunity to participate in the initial pain assessments and assist in multiple treatment modalities, both in the clinic and in the operating room. Given that chronic pain is among the most common presenting complaints seen by physicians, I am confident that this experience will undoubtedly influence the future practices of any student who takes advantage of this unique opportunity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 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".