“Spin” in Observational Studies in Deep Inferior Epigastric Perforator Flap Breast Reconstruction: A Systematic Review
Bibliographic record
Abstract
The deep inferior epigastric artery perforator (DIEP) flap is widely used in autologous breast reconstruction. However, the technique relies heavily on nonrandomized observational research, which has been found to have high risk of bias. "Spin" can be used to inappropriately present study findings to exaggerate benefits or minimize harms. The primary objective was to assess the prevalence of spin in nonrandomized observational studies on DIEP reconstruction. The secondary objectives were to determine the prevalence of each spin category and strategy. Methods: MEDLINE and Embase databases were searched from January 1, 2015, to November 15, 2022. Spin was assessed in abstracts and full-texts of included studies according to criteria proposed by Lazarus et al. Results: There were 77 studies included for review. The overall prevalence of spin was 87.0%. Studies used a median of two spin strategies (interquartile range: 1-3). The most common strategies identified were causal language or claims (n = 41/77, 53.2%), inadequate extrapolation to larger population, intervention, or outcome (n = 27/77, 35.1%), inadequate implication for clinical practice (n = 25/77, 32.5%), use of linguistic spin (n = 22/77, 28.6%), and no consideration of the limitations (n = 21/77, 27.3%). There were no significant associations between selected study characteristics and the presence of spin. Conclusions: The prevalence of spin is high in nonrandomized observational studies on DIEP reconstruction. Causal language or claims are the most common strategy. Investigators, reviewers, and readers should familiarize themselves with spin strategies to avoid misinterpretation of research in DIEP reconstruction.
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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 | 0.049 | 0.187 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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; 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".