Three in every four systematic reviews and meta‐analyses on concomitant anterior cruciate ligament reconstruction and anterolateral complex procedures present at least one type of spin in the abstract
Bibliographic record
Abstract
PURPOSE: Spin is a form of bias that misrepresents research findings, potentially influencing clinical decisions and patient care. This meta-research study evaluates the prevalence and types of spin in abstracts of systematic reviews and meta-analyses comparing anterior cruciate ligament reconstruction (ACLR) with and without lateral extra-articular tenodesis (LET) and anterolateral ligament reconstruction (ALLR). A secondary aim is to assess review quality per AMSTAR-2 criteria and associations between spin and review characteristics. METHODS: A systematic search of PubMed, EMBASE and MEDLINE[Ovid] in May 2024 identified eligible reviews. Two reviewers independently assessed the nine most severe types of spin and methodological quality using AMSTAR-2. Review characteristics, including publication year, total citations, average yearly citations and journal impact factor, were analyzed for associations with spin. RESULTS: Of 24 included reviews, 75% (18/24) contained at least one form of spin, with Type 3 spin, 'Selective reporting or overemphasis on efficacy outcomes favoring the experimental intervention', being the most common (62.5%, 15/24). Reviews with spin had a significantly more recent median publication year (p = 0.011), while those without spin had significantly higher total citation counts (p = 0.021). No significant differences were observed in average yearly citations or impact factors between groups. Reviews with three or more types of spin were published more recently than those with fewer (p = 0.007), with no significant differences in total citations, average yearly citations or impact factor. Most reviews (91.7%) were rated as critically low-quality using AMSTAR-2, with substantial inter-reviewer agreement (κ = 0.839). CONCLUSION: Spin is highly prevalent in systematic reviews and meta-analyses evaluating ACLR with and without LET or ALLR, particularly in recent publications. Given that many reviews exhibit critically low methodological quality, efforts should focus on strengthening adherence to reporting standards and reducing spin to ensure the publication of unbiased evidence that informs clinical practice. LEVEL OF EVIDENCE: Level IV.
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 | 0.048 | 0.118 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.027 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".