Optimizing pain relief in a specialized outpatient palliative radiotherapy clinic: Contributions of a clinical pharmacist
Bibliographic record
Abstract
PURPOSE: Bone metastases are the most common cause of cancer pain, with palliative radiotherapy (RT) the mainstay of treatment. However, relief from RT may be delayed, incomplete, or short-lived and therefore optimized pharmacologic therapy is essential. Our objective was to describe the contribution of the clinical pharmacist (CP) to an outpatient palliative RT clinic. METHODS: The Edmonton Symptom Assessment System, an 11-point scale for measuring nine symptoms, and other validated screening tools were administered, and a medication history performed by the CP. Baseline CP assessment also included opioid toxicity, need for supportive medications, and drug interactions. Anonymized clinical information was collected prospectively and descriptive statistics were compiled including themes of counselling performed by the CP. RESULTS: The CP reviewed 114 patients over 140 clinic visits (01/2007-12/2008). Median age was 68.3 years, 68.4% were male and 36.8% had prostate cancer. All symptoms improved or stabilized in ≥ 80% by 4 weeks. Median pain score was 6/10 (SD 2.6) at baseline, and 2.1/10 (SD 2.4) by week 4. Average morphine equivalent daily dose was 76.8 mg at baseline and 44.5 mg at week 4. CP assessment included screening for opioid toxicity (87.9%), recommending a change in analgesic (28.9%), and liaison with the community pharmacy (17.1%). Medication counselling took place in 84.3% of visits, on bowel routine (85.6% of the time), opioids (82.2%), and hydration (40.7%). CONCLUSIONS: The CP plays a key role in holistic patient assessment and optimization of pharmacologic therapy, contributing to improved symptom control of patients receiving palliative RT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".