Factors associated with improvement in uncontrolled cancer pain without increasing opioid daily dose among patients seen by an inpatient palliative care team.
Bibliographic record
Abstract
9 Background: Increasing total opioid dose is the standard approach for managing uncontrolled cancer pain. Other than simply increasing the opioid dose, palliative care interventions are multidimensional and may improve pain control in the absence of opioid dose increase. The purpose of this study was to determine the proportion of patients referred to our inpatient palliative care (IPC) team who achieved clinically improved pain (CIP) without opioid dose increase. Methods: We reviewed consecutive patients referred to our IPC team. Eligibility criteria included: 1) taking opioid medication; 2) having ≥ 2 consecutive visits with the IPC team; 3) Edmonton Symptom Assessment Scale (ESAS) pain score ≥ 4 at consultation. We assessed patient demographics and clinical variables, including cancer type, opioid prescription data (type, route, oral morphine equivalent daily dose [MEDD]), presence of opioid rotation, psychological consultation, changes in adjuvant medications (e.g., corticosteroids, benzodiazepines, and neuroleptics), and achievement of CIP. Results: Of the 300 patients enrolled, CIP was achieved in 196 (65%) patients. Of CIP patients, 85 (43%) achieved CIP without an increase in MEDD. CIP without MEDD increase was associated with more adjuvant medication changes (P = 0.003), less opioid rotation (P = 0.005), and lower symptom distress scale of ESAS (P = 0.04). Conclusions: Nearly half of patients achieved CIP without MEDD increase, suggesting that multidimensional palliative care intervention is effective in improving pain control in many opioid-tolerant patients without the need to increase the opioid dose.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".