Long-Term Propranolol Use in Severely Burned Pediatric Patients
Bibliographic record
Abstract
OBJECTIVE: To determine the safety and efficacy of propranolol given for 1 year on cardiac function, resting energy expenditure, and body composition in a prospective, randomized, single-center, controlled study in pediatric patients with large burns. BACKGROUND: Severe burns trigger a hypermetabolic response that persists for up to 2 years postburn. Propranolol given for 1 month postburn blunts this response. Whether propranolol administration for 1 year after injury provides a continued benefit is currently unclear. METHODS: One-hundred seventy-nine pediatric patients with more than 30% total body surface area burns were randomized to control (n = 89) or 4 mg/kg/d propranolol (n = 90) for 12 months postburn. Changes in resting energy expenditure, cardiac function, and body composition were measured acutely at 3, 6, 9, and 12 months postburn. Statistical analyses included techniques that adjusted for non-normality, repeated-measures, and regression analyses. P < 0.05 was considered significant. RESULTS: Long-term propranolol treatment significantly reduced the percentage of the predicted heart rate and percentage of the predicted resting energy expenditure, decreased accumulation of central mass and central fat, prevented bone loss, and improved lean body mass accretion. There were very few adverse effects from the dose of propranolol used. CONCLUSIONS: Propranolol treatment for 12 months after thermal injury, ameliorates the hyperdynamic, hypermetabolic, hypercatabolic, and osteopenic responses in pediatric patients. This study is registered at clinicaltrials.gov: NCT00675714.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".