Acute pain management in children
Bibliographic record
Abstract
PURPOSE OF REVIEW: The evidence regarding the efficacy of analgesics available to guide postoperative pain treatment in pediatric patients is limited. Opioid medications are very often an important component of pediatric postoperative pain treatment but have been associated with perioperative complications. We will focus on initiatives aiming to provide effective treatment minimizing the use of opioids and preventing the long-term consequences of pain. RECENT FINDINGS: Interpatient variability in postoperative pain is currently managed by applying protocols or by trial and error, thus often leading to patients being either undertreated or overtreated. Few evidence-based reports are available to guide the use of opioid medications in children, including the prescription of opioids after hospital discharge. Using combinations of nonopioid analgesics in a multimodal approach may limit the need for opioids, thus decreasing the risk of toxicity and dose-related side effects. There is a lack of adequate research in this field, and more specifically on identifying which patient is at higher risk of poor postoperative pain management. SUMMARY: Treatment options have evolved in recent years, including the combinations of multimodal regimens and regional anesthetic techniques. Using combinations of nonopioid analgesics in a multimodal approach may limit the need for opioids.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".