Overinterpretation of Research Findings: Evaluation of “Spin” in Systematic Reviews of Diagnostic Accuracy Studies in High–Impact Factor Journals
Bibliographic record
Abstract
BACKGROUND: To compare the frequency of "spin" in systematic reviews of diagnostic accuracy studies in high-impact journals with the frequency a previously assessed series of reviews. METHODS: Medline was searched from January 2010 to January 2019. Systematic reviews of diagnostic accuracy studies were included if they reported a meta-analysis and were published in a journal with an impact factor >5. Two investigators independently scored each included systematic review for positivity of conclusions and for actual and potential overinterpretation practices. RESULTS: Of 137 included systematic reviews, actual overinterpretation was present in ≥1 form in the abstract in 63 (46%) and in the full-text report in 52 (38%); 108 (79%) contained a form of potential overinterpretation. Compared with the previously assessed series (reviews published 2015-2016), reviews in this series were less likely to contain ≥1 form of actual overinterpretation in the abstract and full-text report or ≥1 form of potential overinterpretation (P < 0.001 for all comparisons). The significance of these comparisons did not persist for actual overinterpretation in sensitivity analysis in which Cochrane systematic reviews were removed. Reviews published in the Cochrane Database of Systematic Reviews were less likely to contain actual overinterpretation in the abstract or the full-text report than reviews in other high-impact journals (P < 0.001 for both comparisons). CONCLUSIONS: Reviews of diagnostic accuracy studies in high-impact journals are less likely to contain overinterpretation or spin. This difference is largely due to the reviews published in the Cochrane Database of Systematic Reviews, which contain spin less often than reviews published in other high-impact journals.
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 | 0.295 | 0.900 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".