A Multicenter Assessment of the Adequacy of Cancer Pain Treatment Using the Pain Management Index
Bibliographic record
Abstract
PURPOSES: Determine adequacy of management of pain secondary to bone metastases by physicians referring to specialized outpatient palliative radiotherapy (RT) clinics in Canada; compare geographic differences in adequacy of pain management and pain severity between these cohorts; compare results with published international literature. METHODS: Prospectively collected data from three participating centers were used to calculate the Pain Management Index (PMI) by subtracting the patient-rated pain score at time of initial clinic visit from the analgesic score. Scores were 0, 1, 2, and 3 when patients reported no pain (0), mild (1-4), moderate (5-6), or severe pain (7-10), respectively, on the Edmonton Symptom Assessment System or Brief Pain Inventory. Analgesic scores of 0, 1, 2, and 3 were assigned for no pain medication, nonopioids, weak opioids, and strong opioids respectively. A negative PMI suggests inadequate pain management. RESULTS: Overall incidence of negative PMI and moderate to severe pain was 25.1% and 70.9% respectively for 2011 patients. Comparing the three participating centers, the incidence of negative PMI was 31.0%, 20.0%, and 16.8% (p < 0.0001), and severe pain was 55.5%, 48.2% and 43.4% (p < 0.0001), these correlated with a negative PMI. Patients referred to our clinics were less likely to be undertreated for their pain when compared to study results from international countries. CONCLUSION: Geographic differences in adequacy of analgesic management for painful bone metastases exist between Canadian specialized outpatient palliative RT clinics and between centers globally. Investigating reasons for these differences may provide insight into solutions to improve quality of life for these patients.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".