Quality appraisal of pediatric health economic evaluations
Bibliographic record
Abstract
OBJECTIVES: This study was undertaken to appraise the quality of published pediatric economic evaluations. METHODS: Two independent reviewers appraised 149 randomly selected pediatric health economic studies. Data were collected from full economic evaluations published between 1980 and 1999. Economic evaluations of interventions, programs, and services aimed at neonates to adolescents were included. The Pediatric Quality Appraisal Questionnaire (PQAQ) was used for appraisal. The PQAQ is a 57-item instrument with 13 domains scored from 0 to 1 and one descriptive domain, each corresponding to a key aspect of health economic methodology. The primary outcome was the score for each domain. Additional analyses examined the global rating, the distribution of analytic technique, and the association between domain score and analytic technique. RESULTS: A total of 38 percent of publications were very good to excellent, whereas 43 percent were fair or worse. Although the Discounting, Target Population, Economic Evaluation, Conclusions, and Comparators domains exhibited good quality (0.74 to 0.78), the papers were of poor quality for Conflict of Interest, Incremental Analysis, and Perspective (0.32 to 0.39). Analytic technique was a significant predictor of quality for study design-related domains, with cost-utility analyses demonstrating the highest domain scores. CONCLUSIONS: Domains closely related to the elements of economic evaluation demonstrated medium to high quality. However, domains related to analysis fared poorly and are worthy of further methodological research to improve the use of health economic methods in children.
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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 | 0.445 | 0.754 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.029 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".