Trends in paediatric health economic evaluation: 1980 to 1999.
Bibliographic record
Abstract
BACKGROUND: Although standard methods for conducting economic evaluations have evolved, little attention has been paid to their application in paediatrics. The Paediatric Economic Database Evaluation (PEDE) Project was conceived to promote research into paediatric health economic methods. AIM: To examine trends in paediatric economic evaluation between 1980 and 1999. METHODS: A comprehensive literature database created for the PEDE project was the source of the data. Descriptive statistics were used to summarise trends. Publication volume, study outcome category, analytical technique, and journal type were examined over the study period. RESULTS: The literature search resulted in 787 full paediatric economic evaluations. The volume of publications increased from 61 to 440 citations per 5 year period. The most common health outcome category was cases of disease/condition/abnormality. Cost-effectiveness analysis (CEA) was the most common technique used, accounting for a majority of evaluations in all time periods. The proportion of studies using CEA increased by 23 percentage points, while the proportion using cost-benefit analysis decreased from 31% in 1980-84 to 12% in 1995-99. Cost-utility analysis was the least common analytical technique. Publication in journals of paediatrics/perinatal medicine was the most common venue for all intervals and increased as a proportion of the total over time. CONCLUSIONS: The growth in publication of paediatric economic evaluations suggests that increasing attention should be paid to the application of health economic methods to a paediatric population to ensure high quality allocation decisions.
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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 | 0.028 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.043 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".