The Pediatric Economic Database Evaluation (PEDE) Project
Bibliographic record
Abstract
BACKGROUND: While standard methods for conducting economic evaluations have evolved, little attention has been paid to the conduct of these studies in special populations such as children. OBJECTIVES: To build a database of pediatric economic evaluations and to examine trends in publication characteristics over a 20-year period. RESEARCH DESIGN: The database was created through a multisource search strategy, manual review, application of inclusion/exclusion criteria, data extraction, and reliability assessment. Descriptive statistics were used to summarize trends in publication volume, disease category, intervention type, and age group between 1980 and 1999. RESULTS: From an initial cut of 5600 citations identified from 12 journal databases, 787 were included as full pediatric economic evaluations. Volume of publications increased 7-fold between 1980 to 1984 and 1995 to 1999 from 61 to 440 citations per 5-year period. Most studies were performed in children aged 1 to 12 years, and studies in infants displayed an increasing frequency. The most common disease category was infective/parasitic, comprising 24% of studies. Studies of congenital anomalies and complications of pregnancy were also prominent. Although health prevention studies were the most prevalent, health treatment studies demonstrated an equal frequency in 1995 to 1999. Most studies consisted of malaria control and vaccination strategies for hepatitis B, Haemophilus influenzae type B, measles, and varicella. CONCLUSIONS: The number of pediatric economic evaluations is steadily increasing with most publications representing health prevention interventions. The Pediatric Economic Database Evaluation (PEDE) Project database will be valuable to health researchers working in methods research and conducting systematic reviews.
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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 | 0.108 | 0.355 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.029 | 0.047 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".