Overinterpretation of Research Findings: Evidence of “Spin” in Systematic Reviews of Diagnostic Accuracy Studies
Bibliographic record
Abstract
BACKGROUND: We wished to assess the frequency of overinterpretation in systematic reviews of diagnostic accuracy studies. METHODS: MEDLINE was searched through PubMed from December 2015 to January 2016. Systematic reviews of diagnostic accuracy studies in English were included if they reported one or more metaanalyses of accuracy estimates. We built and piloted a list of 10 items that represent actual overinterpretation in the abstract and/or full-text conclusion, and a list of 9 items that represent potential overinterpretation. Two investigators independently used the items to score each included systematic review, with disagreements resolved by consensus. RESULTS: We included 112 systematic reviews. The majority had a positive conclusion regarding the accuracy or clinical usefulness of the investigated test in the abstract (n = 83; 74%) and full-text (n = 83; 74%). Of the 112 reviews, 81 (72%) contained at least 1 actual form of overinterpretation in the abstract, and 77 (69%) in the full-text. This was most often a "positive conclusion, not reflecting the reported summary accuracy estimates," in 55 (49%) abstracts and 56 (50%) full-texts and a "positive conclusion, not taking high risk of bias and/or applicability concerns into account," in 47 abstracts (42%) and 26 full-texts (23%). Of these 112 reviews, 107 (96%) contained a form of potential overinterpretation, most frequently "nonrecommended statistical methods for metaanalysis performed" (n = 57; 51%). CONCLUSIONS: Most recent systematic reviews of diagnostic accuracy studies present positive conclusions and a majority contain a form of overinterpretation. This may lead to unjustified optimism about test performance and erroneous clinical decisions and recommendations.
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 | 0.423 | 0.966 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.037 | 0.008 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".