Pharmacological pain management approach for children in the emergency settings: a systematic review
Bibliographic record
Abstract
Background: In emergency settings, prompt and effective pain management is crucial for alleviating distress and ensuring the well-being of pediatric patients. The pharmacological approach plays a central role in managing acute pain in children, but selecting the appropriate medications requires careful consideration of factors such as age, weight, medical history, and the urgency of the situation. Methods: This systematic review aims to comprehensively evaluate the efficacy, safety, and optimal use of pharmacological pain management approaches for children in emergency settings. A comprehensive search across electronic databases, including PubMed, Scopus, Web of Science, and the Cochrane Library was performed. Studies involving pediatric populations (aged 0–16 years) seeking pain management in emergency departments or urgent care settings due to various medical conditions, injuries, or procedures, reporting the utilization of pharmacological interventions were considered only. The quality of the included studies was assessed using the Cochrane Risk of Bias assessment tool to evaluate the methodological quality and risk of bias in randomized controlled trials, while for observational/cohort studies, the Newcastle-Ottawa Scale was utilized to assess the quality and potential biases. Results: The final inclusion incorporated a total of 13 studies published between 2014 and 2024. Diverse pharmaceutical agents, single or in combination, were used through sublingual and intravenous routes for the management of pain among children. These pain management interventions generally led to reduced pain scores; however, the incidence of adverse events varied among studies, highlighting the importance of balancing efficacy with safety in pediatric pain management protocols. Conclusion: Despite the safety and effectiveness of these pharmacological interventions, it is crucial to acknowledge the potential risks of adverse effects linked to certain pharmaceutical agents. This emphasizes the importance of conducting future clinical research in this field, particularly randomized trials and cohort studies, to gather evidence-based data that can contribute to the creation of extremely safe therapeutic protocols for this susceptible group.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| 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".