Discrepancies between Registered and Published Primary and Secondary Outcomes in Randomized Controlled Trials within the Plastic Surgery Literature: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Recent studies have identified a high incidence of discrepancy between registered and published outcomes in registered medical and surgical randomized controlled trials. This has not yet been studied in the plastic surgery literature. METHODS: The authors systematically assessed plastic surgery randomized controlled trials published between 2012 and 2016 in seven high-impact plastic surgery journals. Data were collected from the registration website and published articles using a standardized data extraction form. RESULTS: A total of 145 randomized controlled trials were identified, with a 39 percent trial registration rate (n = 57). Forty-nine trials were included in the final analysis. Forty-three (88 percent) had a discrepancy between registered and published outcomes: 26 (53 percent) for primary outcome(s), and 39 (80 percent) for secondary outcome(s). The number of discrepancies in an individual trial ranged from one to seven for primary outcomes and one to 12 for secondary outcomes. Aesthetic surgery had the largest number of trials with outcome discrepancies (n = 15). The prevalence of unreported registered outcomes was 13 percent for primary outcomes and 38 percent for secondary outcomes. Registered nonsignificant primary outcomes were published as nonsignificant secondary outcomes in 30 percent of trials. Publishing new nonregistered secondary outcomes (65 percent) and changing the assessment timing of published primary outcomes (61 percent) were the most common types of discrepancies. Discrepancies favored a statistically significant positive outcome in 19 (44 percent) of the 43 trials with an outcome discrepancy. Discrepancies that resulted in published outcomes with improved patient relevance were found in eight trials (16 percent) for primary outcome discrepancies and 14 trials (29 percent) for secondary outcome discrepancies. CONCLUSIONS: The plastic surgery literature has high rates of discrepancies between registered and published trial outcomes. Outcome reporting discrepancy is even more problematic for secondary outcomes, an area of analysis that has previously been poorly studied. The high rate of discrepancy change favoring a statistically significant outcome and more patient-relevant outcomes may indicate the pressure to demonstrate significant results to be accepted for publication in high-impact journals.
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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 | 0.319 | 0.715 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.012 |
| Bibliometrics | 0.031 | 0.030 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".