Evaluation of “Spin” in the Abstracts of Systematic Reviews and Meta-Analyses of Therapeutic Interventions Published in High-Impact Plastic Surgery Journals: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: "Spin" is a form of reporting bias where there is an misappropriated presentation of study results, often overstating efficacy or understating harms. Abstracts of systematic reviews in other clinical domains have been demonstrated to employ spin, which may lead to clinical recommendations that are not justified by the literature. OBJECTIVES: The objective of this study was to determine the prevalence of spin strategies in abstracts of plastic surgery systematic reviews. METHODS: A literature search was conducted using MEDLINE, Embase, and CENTRAL, to identify all systematic reviews published in the top five plastic surgery journals from 2015-2021. Screening, data extraction, and spin analysis were performed by two independent reviewers. Data checking of the spin analysis was performed by a plastic surgery resident with graduate level training in clinical epidemiology. RESULTS: From an initial search of 826 systematic reviews, 60 systematic reviews and meta-analyses were included in this study. Various types of spin were identified in 73% of systematic review abstracts (n=44). "Conclusion claims the beneficial effect of the experimental treatment despite high risk of bias in primary studies," was the most prevalent type of spin and was identified in 63% of systematic reviews (n=38). There were no significant associations between the presence of spin and study characteristics. CONCLUSIONS: The present study found that 73% of abstracts in plastic surgery systematic reviews contain spin. Although systemic reviews represent the highest level of evidence, readers should be aware of types of "spin" when interpreting results and incorporating recommendations into patient care.
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 | 0.359 | 0.658 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.029 | 0.034 |
| Bibliometrics | 0.055 | 0.039 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".