Assessing “Spin” in Urology Randomized Controlled Trials With Statistically Nonsignificant Primary Outcomes
Bibliographic record
Abstract
PURPOSE: "Spin" refers to a form of language manipulation that positively reflects negative findings or downplays potential harms. Spin has been reported in randomized controlled trials of other surgical specialties, which can lead to the recommendation of subpar or ineffective treatments. The goal of this study was to characterize spin strategies and severity in statistically nonsignificant urology randomized controlled trials. MATERIALS AND METHODS: A comprehensive search of MEDLINE and Embase for the top 5 urology journals, major urology subspecialty journals, and high-impact nonurology journals from 2019 to 2021 was conducted. Statistically nonsignificant randomized controlled trials with a defined primary outcome were included. Screening, data extraction, and spin assessment were performed in duplicate by 2 independent reviewers. RESULTS: From the database search of 4,339 studies, 46 trials were included for analysis. Spin was identified in 35 studies (76%), with the majority of abstracts (n = 26, 57%) and main texts (n = 35, 76%) containing some level of spin. "Obscuring the statistical nonsignificance of the primary outcome and focusing on statistically significant secondary results" was the most frequently used strategy in abstracts, while "other" strategies not previously defined were the most commonly used strategies in main texts. Moderate or high spin severity was identified in 21 (46%) abstract and 22 (48%) main text conclusions. CONCLUSIONS: Overall, our results suggest that 76% of statistically nonsignificant urology randomized controlled trials contained some level of spin. Readers and writers should be aware of common spin strategies when interpreting nonsignificant results and critically appraise the significance of results when making decisions for clinical practice.
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 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 | 0.548 | 0.782 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.019 |
| Bibliometrics | 0.029 | 0.021 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".