Evaluation of Spin in the Abstracts of Systematic Reviews and Meta-Analyses Focused on the Treatment of Acne Vulgaris: Cross-Sectional Analysis
Bibliographic record
Abstract
Background Spin is the misrepresentation of study findings, which may positively or negatively influence the reader’s interpretation of the results. Little is known regarding the prevalence of spin in abstracts of systematic reviews, specifically systematic reviews pertaining to the management and treatment of acne vulgaris. Objective The primary objective of this study was to characterize and determine the frequency of the most severe forms of spin in systematic review abstracts and to evaluate whether various study characteristics were associated with spin. Methods Using a cross-sectional study design, we searched PubMed and EMBASE for systematic reviews focusing on the management and treatment of acne vulgaris. Our search returned 316 studies, of which 36 were included in our final sample. To be included, each systematic review must have addressed either pharmacologic or nonpharmacologic treatment of acne vulgaris. These studies were screened, and data were extracted in duplicate by two blinded investigators. We analyzed systematic review abstracts for the nine most severe types of spin. Results Spin was present in 31% (11/36) of abstracts. A total of 12 examples of spin were identified in the 11 abstracts containing spin, with one abstract containing two instances of spin. The most common type of spin, selective reporting of or overemphasis on efficacy outcomes or analysis favoring the beneficial effect of the experimental intervention, was identified five times (5/12, 42%). A total of 44% (16/36) of studies did not report a risk of bias assessment. Of the 11 abstracts containing spin, six abstracts (55%) had not reported a risk of bias assessment or performed a risk of bias assessment but did not discuss it. Spin in abstracts was not significantly associated with a specific intervention type, funding source, or journal impact factor. Conclusions Spin is present in the abstracts of systematic reviews and meta-analyses covering the treatment of acne vulgaris. This paper raises awareness of spin in abstracts and emphasizes the importance of its recognition, which may lead to fewer incidences of spin in future studies.
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 | 0.243 | 0.459 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.029 | 0.055 |
| Bibliometrics | 0.031 | 0.025 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".