Systematic Review of Economic Evaluations in Plastic Surgery
Bibliographic record
Abstract
BACKGROUND: Economic evaluations are quantitative methods comparing alternative interventions using cost data and expected outcomes. They are used to recommend/dissuade adoption of new surgical interventions and compare different clinical pathways, settings (inpatient/outpatient), or time horizons to determine which procedure may be more cost-effective. The objective of this systematic review was to describe all published English economic evaluations related to a plastic surgery domain. METHODS: A comprehensive English literature review of the MEDLINE, EMBASE, The Cochrane Library, Health Economic Evaluations Database, Ovid Health Star, and Business Source Complete databases was conducted (January 1, 1986, to June 15, 2012). Articles were assessed by two independent reviewers using predefined data fields and selected using specific inclusion criteria. Extracted information included country of origin, journal, and date of publication. Domain of plastic surgery and type of economic evaluation were ascertained. RESULTS: Ninety-five articles were included in the final analysis, with cost analysis being the most common economic evaluation (82 percent). Full economic evaluations represented 18 percent. General cutaneous disorders/burns (24 percent), breast surgery (20 percent), and "multiple" (15 percent) were the top domains studied. Authors were predominantly based in the United States (56 percent) and published in the journal Plastic and Reconstructive Surgery (22 percent), with a significant proportion (40 percent) published in the last 5 years. CONCLUSIONS: Partial economic assessments (cost analyses) with limited benefit represent the majority of economic evaluations in plastic surgery. This suggests an urgent need to alert plastic surgeons to the advantages of full economic evaluations (cost-effectiveness and cost utility analyses) and the need to perform such rigorous analyses.
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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 | 0.240 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.025 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".