The McGill University Health Centre Cancer Pain Clinic: A Retrospective Analysis of an Interdisciplinary Approach to Cancer Pain Management
Bibliographic record
Abstract
Context. The McGill University Health Center (MUHC) Cancer Pain Clinic offers an interdisciplinary approach to cancer pain management for patients. The core team includes a nurse clinician specialist in oncology and palliative care, a palliativist, an anaesthetist, and a radiation oncologist. This tailored approach includes pharmacological and nonpharmacological therapies offered concurrently in an interdisciplinary fashion. Objectives. Description of the interdisciplinary MUHC cancer pain approach and analysis of treatments and outcomes. Methods. A retrospective analysis of new outpatients completing two subsequent visits (baseline and follow-ups: FU1, FU2) was conducted. Variables included (a) symptom severity measured by the Edmonton Symptom Assessment Scale, (b) pain and disability measured with the Brief Pain Inventory, and (c) analgesic plan implementation including pharmacological and nonpharmacological therapies. Results. 71 charts were reviewed. Significant pain relief was achieved consistently at FU1 and FU2. The average pain severity decreased by 2 points between initial assessment and FU2. More than half (53%) of patients responded with a pain reduction greater than 30%. Severity of other symptoms (i.e., fatigue, nausea, depression, and anxiety) and disability also decreased significantly at FU2. The total consumption of opioids remained stable; however, the consumption of short acting preparations decreased by 52% whereas the prescription of nonopioid agents increased. Beyond drug management, 60% of patients received other analgesic therapies, being the most common interventional pain procedures and psychosocial approaches. Conclusion. The MUHC interdisciplinary approach to cancer pain management provides meaningful relief of pain and other cancer-related symptoms and decreases patients' disability.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".