Comparative Effectiveness of Pediatric Integrative Medicine: A Pragmatic Cluster-Controlled Trial
Bibliographic record
Abstract
Symptoms of pain, nausea/vomiting, and anxiety (PNVA) are highly prevalent in pediatric inpatients. Poorly managed symptoms can lead to decreased compliance with care, and prolonged recovery times. Pharmacotherapy used to manage PNVA symptoms is of variable effectiveness and carries safety risks. Complementary therapies to manage these symptoms are gaining popularity due to their perceived benefits and low risk of harm. Pediatric integrative medicine (PIM) is the combination of complementary therapies with conventional medicine in pediatric populations. A two-arm, cluster-controlled, pragmatic clinical trial was carried out to compare the effectiveness of a PIM service in conjunction with usual care, versus usual care only to treat PNVA symptoms in hospitalized pediatric patients. The primary outcome was the improvement of PNVA symptom severity using a 10-point numerical rating scale. Participant enrollment occurred between January 2013 and January 2016. A total of 872 participants (usual care n = 497; PIM n = 375) were enrolled. The PIM therapies significantly reduced PNVA symptom severity (p < 0.001). This study found that a hospital-based PIM service is both safe and effective for alleviating PNVA symptoms. Future research should carry out this work in other pediatric inpatient divisions, and in other sites to determine the reproducibility of findings.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".