“Spin” in Plastic Surgery Randomized Controlled Trials with Statistically Nonsignificant Primary Outcomes: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: "Spin" refers to a manipulation of language that implies benefit for an intervention when none may exist. Randomized controlled trials (RCTs) in other fields have been demonstrated to employ spin, which can mislead clinicians to use ineffective or unsafe interventions. This study's objective was to determine the strategies, severity, and extent of spin in plastic surgery RCTs with nonsignificant primary outcomes. METHODS: A literature search of the top 15 plastic surgery journals using MEDLINE was performed (2000 through 2020). Parallel 1:1 RCTs with a clearly identified primary outcome showing statistically nonsignificant results ( P > 0.05) were included. Screening, data extraction, and spin analysis were performed by two independent reviewers. The spin analysis was then independently assessed in duplicate by two plastic surgery residents with graduate-level training in clinical epidemiology. RESULTS: From 3497 studies identified, 92 RCTs were included in this study. Spin strategies were identified in 78 RCTs (85%), including 64 abstracts (70%) and 77 main texts (84%). Severity of spin was rated moderate or high in 43 abstract conclusions (47%) and 42 main text conclusions (46%). The most identified spin strategy in the abstract was claiming equivalence for statistically nonsignificant results (26%); in the main text, focusing on another objective (24%). CONCLUSIONS: This study suggests that 85% of statistically nonsignificant RCTs in plastic surgery employ spin. Readers of plastic surgery research should be aware of strategies, whether intentional or unintentional, used to manipulate language in reports of statistically nonsignificant RCTs when applying research findings to clinical practice.
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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 | 0.251 | 0.565 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.018 | 0.019 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".