Conversion ratios for opioid switching: a pragmatic study
Bibliographic record
Abstract
Abstract Background: The final conversion ratios among opioids used for successful switching are unknown. The aim of this study was to determine the initial and final conversion ratios used for a successful opioid switching in cancer patients, and eventual associated factors.Methods: Ninety-five patients who were successfully switched were evaluated. The following data were collected: age, gender, Karnofsky performance score, primary cancer, cognitive function, the presence of neuropathic, and incident pain. Opioids, route of administration, and their doses expressed in oral morphine equivalents used before OS, were recorded as well as opioids use for starting opioid switching, and at time of stabilization. Physical and psychological symptoms were routinely evaluated by Edmonton Symptom Assessment Scale.Results: No statistical changes were observed between the initial conversion ratios and those achieved at time of stabilization for all the sequences of opioid switching. When considering patients switched to methadone, there was no association between factors taken into considerations.Conclusion: Opioid switching is a highly effective and safe technique, improving analgesia and reducing the opioid-related symptom burden. The final conversion ratios were not different from those used for starting opioid switching. Patients receiving higher doses of opioids should be carefully monitored for individual and unexpected responses in an experienced palliative care unit, particularly those switched to methadone. Future studies should provide data regarding the profile of patients with difficult pain to be hospitalized.
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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 | 0.036 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".