Methadone for Analgesia in Children with Life-Limiting Illness: Experience from a Tertiary Children’s Health Service
Bibliographic record
Abstract
Methadone has the potential to assist in the management of pain in children with life-limiting illness, but its use is limited by its complex pharmacokinetic profile and limited research on its use in children. This is a retrospective review of the use of methadone as an analgesic in 16 children with life-limiting illness. Efficacy, dosing and side effect profile were analysed. Fifteen (94%) patients had improvements in their analgesia with minimal observed adverse effects. Patients were either rapidly converted from a prior opioid in one change or received methadone as an adjunct medication. Conversions were calculated using ratios frequently in the range of 10:1 to 20:1 from the oral morphine equivalent total daily dose (MEDD). Adjunct initial dosing was a low dose trial, often beginning with 1 mg at night. Only two patients required a dose adjustment due to side effects attributed to methadone. This was despite the cohort having significant underlying illnesses, extensive concurrent medications, and high methadone dosing where needed. Analysis of dosing and ratios indicates that an individualised approach is required. Based on this and on the infrequency of methadone use in this population, specialist assistance with dosing is recommended. Further research, including prospective and pharmacokinetic studies, is recommended.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".