A Titration Strategy Is Needed To Manage Breakthrough Cancer Pain Effectively: Observations from Data Pooled from Three Clinical Trials
Bibliographic record
Abstract
BACKGROUND: Breakthrough pain is a prevalent and serious problem in patients with cancer. However, it is not known how best to predict the effective dose of breakthrough opioid for any given patient. METHODS: Data were pooled and reanalyzed from three large, randomized clinical trials of the rapidly absorbed oral transmucosal fentanyl citrate lozenges (OTFC) in which patients were carefully titrated to an optimal OTFC dose. The relationships between the optimal OTFC dose, patients' previous opioid dose, 24-hour total opioid, and patient characteristics were explored to determine whether the optimal OTFC dose can be predicted based on pretreatment clinical factors. RESULTS: The cohort included 188 patients within the three trials whose breakthrough pain was effectively managed with OTFC. Prior to entry into the trial, the average breakthrough opioid dose in the 188 patients was 12% of the daily dose of scheduled opioid but strikingly, ranged from 1%-72%. The optimal OTFC dose was poorly correlated with patients' scheduled or previous breakthrough opioid doses. The only clinically meaningful correlation was that the average final OTFC dose significantly decreased with increasing age. Overall, there was enormous interindividual variability in patients' dose requirements for breakthrough pain. CONCLUSIONS: This is the largest study to date of the relationship between clinical variables and the effective dose of OTFC when titrated to effect for breakthrough cancer pain. These results suggest that use of breakthrough medication should routinely be individualized with a titration strategy separate from the around-the-clock medication, according to each patient's response to their breakthrough opioid.
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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 | 0.022 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 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".