Comparison of a novel methadone rotation method with other commonly used methods
Bibliographic record
Abstract
OBJECTIVES: To compare a novel method of methadone rotation used in a specialist palliative care inpatient unit (SPCU) in Cork, Ireland, with rapid titration methods using Perth and Brisbane Protocols as well as the Edmonton method of methadone rotation. METHODS: A retrospective chart review was performed in March-June 2022. All patients who completed rotation to methadone during 2018-2019 in the SPCU were included. 2018-2019 was selected to study a population not affected by the coronavirus pandemic. Oral morphine equivalent (OME) was calculated using the opioid conversion chart. From the OME, the expected daily methadone dose was calculated using the Perth, Brisbane and Edmonton methods. These figures were then compared directly with the actual methadone doses achieved using our dosing schedule. RESULTS: A comparison of the expected doses using the Perth and Brisbane rapid titration protocols and stable daily dose achieved revealed that the stable methadone dose was significantly lower than both rapid titration protocols (p=<0.0001) and (p=0.0035, respectively). However, a comparison of the expected dose using the Edmonton method and the dose achieved did not determine any significant difference (p=0.7602). CONCLUSIONS: This is the first evaluation of a novel Irish method of methadone rotation and demonstrates a lower overall daily methadone dose compared with established protocols.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".