Morphine versus hydromorphone in pediatrics: a narrative review of latest indications and optimal use in neonates and children
Bibliographic record
Abstract
The management of pain in pediatrics is multimodal and includes non-pharmacologic and pharmacologic approaches. Opioids, and particularly morphine and hydromorphone, are frequently used to treat moderate-to-severe pain. The goals of this review are to describe the pharmacological characteristics of both drugs, to cover the latest evidence of their respective indications, and to promote their safe use in pediatrics. Morphine is the most studied opioid in children and is known to be safe and effective. Morphine and hydromorphone can be used to manage acute pain and are usually avoided when treating chronic non-cancer pain. Current evidence suggests that both opioids have a similar efficacy and adverse effect profile. Hydromorphone has not been studied in neonates but in some centers, it has been used instead of morphine for certain patients. In palliative care, the use of opioids is often indicated and their benefits extend beyond analgesia; indications include treatment of central neuropathic pain in children with severe neurologic impairment and treatment of respiratory distress in the imminently dying patients. The longstanding belief that the use of well-titrated opioids hastens death should be abandoned as robust evidence has shown the opposite. With the current opioid crisis, a responsible use of opioids should be promoted, including limiting the opioid prescription to the patient's anticipated needs, optimizing a multimodal analgesic plan including the use of non-pharmacological measures and non-opioid medications, and providing information on safe storage and disposal to patients and families. More data is needed to better guide the use of morphine and hydromorphone in children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".