A Systematic Review of Power and Sample Size Reporting in Randomized Controlled Trials within Plastic Surgery
Bibliographic record
Abstract
BACKGROUND: The randomized controlled trial is a reliable study design for assessing the effectiveness of a surgical intervention, provided it is adequately powered. This systematic review examines the appropriateness of reporting of power and sample size in randomized controlled trials within the plastic surgery literature. METHODS: Original randomized controlled trials published from January of 1990 to December of 2010 in nine high-impact plastic surgery journals were appraised. The data extracted from each study included calculation of power and sample size, number of patients, and effect size. A Jadad score was calculated, providing a quality assessment of the randomized controlled trial. RESULTS: : Of the 736 original articles, 463 met the inclusion criteria; 88 (19.0 percent) of these 463 reported performing a priori power analysis or sample size calculation. Of these 88 studies, 68 (77.3 percent) had an adequate sample size. In most studies, a standard of 0.05 for the type I error and 0.20 for type II error was used. There has been some improvement in the reporting of power and sample size in the decades from 1990 to 2010. CONCLUSIONS: Nineteen percent of 463 randomized controlled trials in the plastic surgery literature reported performing an a priori power analysis or sample size calculation. The implication is that when we read the results of a published randomized controlled trial in plastic surgery, in 81 percent of cases we cannot trust the findings. Although the reporting of power and sample size has improved in the last decade, it is still inadequate. Lack of such reporting casts doubt on the validity (truthfulness) of the study's findings. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.775 | 0.995 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.260 | 0.043 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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; both teacher heads 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".