Reporting Adverse Events in Plastic Surgery: A Systematic Review of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: Accurate knowledge of adverse events is critical for evaluation of the safety of interventions. Historically, adverse events in surgical trials have been poorly reported. The objective of this study was to systematically evaluate the reporting of adverse events in randomized controlled trials in the plastic surgery literature. METHODS: Two independent reviewers conducted a systematic search using MEDLINE, Embase, and Scopus of the top seven plastic surgery journals with the highest impact factors. Randomized controlled trials describing a potentially invasive treatment, published between January of 2012 and December of 2016, were included. RESULTS: One hundred forty-five randomized controlled trials involving 10,266 patients were included, of which 30 percent were registered. Anticipated adverse events were clearly defined in 15 percent of trials, and in 70 percent it was not clear who would be documenting adverse events. Furthermore, 72 percent of randomized controlled trials reported the occurrence of adverse events, of which 61 percent failed to report events occurring in the intrainterventional period. Binary logistic regression revealed that funded randomized controlled trials were 4.04 times more likely to report adverse events compared with nonfunded randomized controlled trials (95 percent CI, 1.41 to 10.83; p = 0.009). CONCLUSIONS: The authors' findings suggest the need for reporting standards for adverse events in the plastic surgery literature, as such reporting remains heterogeneous and is lacking rigor. Improved quality and transparency are needed to strengthen evidence-based practice and permit a balanced intervention assessment. This study provides a set of recommendations aimed at improving adverse event reporting.
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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 | 0.213 | 0.521 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.016 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 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".