Patterns of Opioid Prescription, Use, and Costs Among Patients With Advanced Cancer and Inpatient Palliative Care Between 2008 and 2014
Bibliographic record
Abstract
PURPOSE: An understanding of opioid prescription and cost patterns is important to optimize pain management for patients with advanced cancer. This study aimed to determine opioid prescription and cost patterns and to identify opioid prescription predictors in patients with advanced cancer who received inpatient palliative care (IPC). MATERIALS AND METHODS: We reviewed data from 807 consecutive patients with cancer who received IPC in each October from 2008 through 2014. Patient characteristics; opioid types; morphine equivalent daily dose (MEDD) in milligrams per day of scheduled opioids before, during, and after hospitalization; and in-admission opioid cost per patient were assessed. We determined symptom changes between baseline and follow-up palliative care visits and the in-admission opioid prescription predictors. RESULTS: A total of 714 (88%) of the 807 patients were evaluable. The median MEDD per patient decreased from 150 mg/d in 2008 to 83 mg/d in 2014 ( P < .001). The median opioid cost per patient decreased and then increased from $22.97 to $40.35 over the 7 years ( P = .03). The median MEDDs increased from IPC to discharge by 67% ( P < .001). The median Edmonton Symptom Assessment Scale pain improvement at follow-up was 1 ( P < .001). Younger patients with advanced cancer (odds ratio [OR[, 0.95; P < . 001) were prescribed higher preadmission MEDDs (OR, 1.01; P < .001) more often in the earlier study years (2014 v 2009: OR, 0.18 [ P = .004] v 0.30 [ P = .02]) and tended to use high MEDDs (> 75 mg/d) during hospitalization. CONCLUSION: The MEDD per person decreased from 2008 to 2014. The opioid cost per patient decreased from 2008 to 2011 and then increased from 2012 to 2014. Age, prescription year, and preadmission opioid doses were significantly associated with opioid doses prescribed to patients with advanced cancer who received IPC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".