Assessment of “Spin” in the Abstracts of Systematic Reviews in Leading Arthroplasty Journals Over the Past 10 Years: A Cross-Sectional Methodological Study
Bibliographic record
Abstract
Background Systematic reviews (SRs) are vital for evidence consolidation in clinical research, utilized for their robust, reproducible methodology. SRs are prone to misrepresentation as they are based on primary studies and the interpretation of their authors. This issue can be particularly significant in arthroplasty, where procedures significantly impact patient quality of life. We aim to determine the prevalence of spin and its associated study characteristics in the top 3 highest impact English arthroplasty specific journals. Methods We searched PubMed, MEDLINE, and Embase for SRs and meta-analyses published from March 26, 2015, to March 26, 2025. Two assessors classified spin types based on Yavchitz et al. (2016). Descriptive statistics were computed to determine spin prevalence, and chi-square (χ2) analysis evaluated associations with bibliometric factors. Results Spin was present in 48 of 52 SRs (92.3%), with severe spin in 33 of 52 (63.5%). The most common types included type 5 (claims of benefit despite high risk of bias) in 45 of 52 (86.5%) and type 9 (claims of benefit despite reporting bias) in 32 of 52 (61.5%). Type 9 spin was significantly more common in SRs involving randomized controlled trials (88.2%) compared to nonrandomized studies (48.6%, P = .014). Conclusions Over the past decade, more than 90% of SRs published in high-impact arthroplasty journals exhibited spin in their abstracts. The most common form involved overstating treatment benefits despite high risk of bias in primary studies, while spin related to reporting bias was significantly more frequent in SRs of randomized evidence.
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 | 0.289 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".