The Role of OROS<sup>®</sup>Hydromorphone in the Management of Cancer Pain
Bibliographic record
Abstract
The vast majority of cancer patients experience pain, and treatment with opioids offers the most effective option for pain management. Long-lasting opioid formulations are usually used as cancer pain management strategies. This review surveys the available literature on the only available once-daily sustained-release formulation of hydromorphone, and its use in cancer pain management. Sustained-release (SR) formulations have a more consistent opioid plasma concentration, thereby minimizing the peaks and troughs associated with immediate-release opioid formulations. OROS hydromorphone (Jurnista, Janssen Pharmaceuticals, NV, Beerse, Belgium) releases hydromorphone over a 24-hour dosing period. Studies comparing its efficacy with other opioids such as morphine and oxycodone found comparable results overall. Recent trials have provided evidence of decreased rescue medication use for breakthrough pain, a good safety profile, and quality of life benefits. It appears to be an efficacious and well-tolerated treatment. The pharmacokinetics of OROS hydromorphone are linear and dose-proportional, and only minimally affected by the presence or absence of food. In addition, the SR properties of OROS hydromorphone are maintained in the presence of alcohol, with no dose dumping of hydromorphone. This formulation shows promise as an addition to cancer pain management strategies, although further randomized, double-blind trials are needed to confirm this.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".