A Methodological Analysis of the Plastic Surgery Cost-Utility Literature Using Established Guidelines
Bibliographic record
Abstract
BACKGROUND: Cost-utility studies, common in medicine, are rare within plastic surgery despite their capability of measuring the value of procedures by considering the societal costs of improving quality of life. The objectives of this study were to analyze the design quality of the plastic surgery cost-utility literature and to identify areas of needed improvement for future studies. METHODS: A scoring tool was constructed based on the Recommendations of the Panel on Cost-Effectiveness in Health and Medicine. A PubMed search through October of 2012 was conducted for English-language plastic surgery utility studies. Articles were selected using two inclusion criteria and evaluated using the scoring tool. RESULTS: A 9-point scoring tool was created, and 37 publications were selected. Their average score was 3 out of 9 points. Thirty studies (81 percent) used population preferences in utility measurements. Fifteen studies (41 percent) measured costs, but only four (11 percent) included indirect costs and only five (14 percent) applied discount rates to calculate the value of treatments over time. Three studies (8 percent) earned zero points. The highest scoring study earned 8 points. CONCLUSIONS: The identified studies manifest the potential of cost-utility analyses in plastic surgery. Nonetheless, they are inconsistent in applying established cost-utility guidelines, especially in measuring costs and conducting recommended sensitivity analysis. Following this simple scoring tool can help future studies achieve some necessary improvements.
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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 | 0.460 | 0.734 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.015 |
| Bibliometrics | 0.076 | 0.059 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".