Improving the Science in Plastic Surgery
Bibliographic record
Abstract
SUMMARY: In 1906, George Bernard Shaw criticized the medical profession for its lack of science and compassion. Since then, advances in both medical and surgical subspecialties have improved quality of patient care. Unfortunately, the reporting of these advances is variable and is frequently biased. Such limitations lead to false claims, wasted research dollars, and inability to synthesize and apply evidence to practice. It was hoped that the introduction of evidence-based medicine would improve the quality of health care and decrease health dollar waste. For this to occur, however, credible "best evidence"-one of the components of evidence-based medicine-is required. This article provides a framework for credible research evidence in plastic surgery, as follows: (1) stating the clinical research question, (2) selecting the proper study design, (3) measuring critical (important) outcomes, (4) using the correct scale(s) to measure the outcomes, (5) including economic evaluations with clinical (effectiveness) studies, and (6) reporting a study's results using the Enhancing the Quality and Transparency of Health Research, or EQUATOR, guidelines. Surgeon investigators are encouraged to continue improving the science in plastic surgery by applying the framework outlined in this article. Improving surgical clinical research should decrease resource waste and provide patients with improved evidence-based care.
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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 | 0.037 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 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".