The McGill University Health Centre interdisciplinary approach to cancer pain management: Description of treatments and outcomes.
Bibliographic record
Abstract
190 Background: Cancer-related symptom management is best achieved by interdisciplinary teams. In 2011, the MUHC launched an interdisciplinary Cancer Pain Clinic including palliative care, anesthesia, radiation oncology and nursing with rapid access to physiotherapy, occupational therapy and psychosocial oncology. Methods: We retrospectively analysed all new outpatients completing two subsequent visits since March 2013. Variables included a) symptom severity with the Edmonton Symptom Assessment Scale (ESAS), b) pain with the Brief Pain Inventory (BPI) and c) treatment including medication (type, formulation, dose of opioids) of non-pharmacological approaches. Results: 71 patients were analysed. Symptom management: Severity of pain and other five symptoms decreased significantly at V2 or V3 (Table). Pain significantly decreased in all four BPI categories. One third of patients had ≥50% pain relief at V2 or V3. Number of severe pain cases decreased (45% at V1 to 18% at V3) in parallel to an increase of mild pain cases (11% at V1, 41% at V3). Treatments: Acetaminophen, anticonvulsants and NSAIDs were the most common non-opioid drugs. Opioid prescription remained constant at an 80% yet the ratio between short acting (SA) and long acting (LA) changed at V3 compared to V1 (V1: SA/LA=76/44; V3: SA/LA=48/58) and the morphine equivalent daily doses decreased (V1: 100±194 mg, V2: 84±158mg and V3: 65±80mg). Among non-pharmacological methods, 28% of patients received interventional procedures, 18% psychotherapy and 12% palliative radiotherapy. Conclusions: We believe that the pain and other symptom improvement observed after three visits along with a lower opioid consumption is a result of the interdisciplinary approach offered. [Table: see text]
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".