Economic Evaluations of Health Care Interventions in Pediatric Hospital Care
Bibliographic record
Abstract
The hospital medicine movement thrives in a health care environment committed to providing high-quality, safe, and value-based care. Hospitalists and hospitals continually grapple with many decisions regarding adopting new interventions and deadopting established ones. These decisions span the gamut from tests, treatments, and supportive care, to care models. Traditionally, the choice to adopt one intervention over another is commonly thought of in terms of its direct impact on patient outcomes, benefits, and harms. However, the evolving landscape of health care, characterized by increasing constraints on resources necessitates a broader perspective, one that includes a thorough consideration of the economic implications. The goal is not to minimize costs but rather to maximize value, outcomes achieved for money spent. Economic evaluations of health care interventions can provide this information by quantifying value and assisting health care providers, hospitals, and health systems in deciding which intervention to adopt. Economic evaluations deal with both inputs (ie, costs) and outputs (ie, consequences). Few economic evaluations in pediatric hospital medicine have been published, and many clinicians are unfamiliar with them. This paper discusses the economic evaluation of health care interventions with special attention to the pediatric hospitalist and hospital care. The paper aims to give readers an understanding of the key concepts underlying economic evaluations.
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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 | 0.051 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".