How Safe Are Common Analgesics for the Treatment of Acute Pain for Children? A Systematic Review
Bibliographic record
Abstract
Background. Fear of adverse events and occurrence of side effects are commonly cited by families and physicians as obstructive to appropriate use of pain medication in children. We examined evidence comparing the safety profiles of three groups of oral medications, acetaminophen, nonsteroidal anti-inflammatory drugs, and opioids, to manage acute nonsurgical pain in children (<18 years) treated in ambulatory settings.Methods. A comprehensive search was performed to July 2015, including review of national data registries. Two reviewers screened articles for inclusion, assessed methodological quality, and extracted data. Risks (incidence rates) were pooled using a random effects model.Results. Forty-four studies were included; 23 reported on adverse events. Based on limited current evidence, acetaminophen, ibuprofen, and opioids have similar nausea and vomiting profiles. Opioids have the greatest risk of central nervous system adverse events. Dual therapy with a nonopioid/opioid combination resulted in a lower risk of adverse events than opioids alone.Conclusions. Ibuprofen and acetaminophen have similar reported adverse effects and notably less adverse events than opioids. Dual therapy with a nonopioid/opioid combination confers a protective effect for adverse events over opioids alone. This research highlights challenges in assessing medication safety, including lack of more detailed information in registry data, and inconsistent reporting in trials.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".