The Misconception of Case-Control Studies in the Plastic Surgery Literature: A Literature Audit
Bibliographic record
Abstract
BACKGROUND: Case-control study designs are commonly used. However, many published case-control studies are not true case-controls and are in fact mislabeled. The purpose of this study was to identify all case-control studies published in the top three plastic surgery journals over the past 10 years, assess which were truly case-control studies, clarify the actual design of the articles, and address common misconceptions. METHODS: MEDLINE, Embase, and Web of Science databases were searched for case-control studies in the three highest-impact factor plastic surgery journals (2005 to 2015). Two independent reviewers screened the resulting titles, abstracts, and methods, if applicable, to identify articles labeled as case-control studies. These articles were appraised and classified as true case-control studies or non-case-control studies. RESULTS: The authors found 28 articles labeled as case-control studies. However, only six of these articles (21 percent) were truly case-control designs. Of the 22 incorrectly labeled studies, one (5 percent) was a randomized controlled trial, three (14 percent) were nonrandomized trials, two (9 percent) were prospective comparative cohort designs, 14 (64 percent) were retrospective comparative cohort designs, and two (9 percent) were cross-sectional designs. The mislabeling was worse in recent years, despite increases in evidence-based medicine awareness. CONCLUSIONS: The majority of published case-control studies are not in fact case-control studies. This misunderstanding is worsening with time. Most of these studies are actually comparative cohort designs. However, some studies are truly clinical trials and thus a higher level of evidence than originally proposed.
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 | 0.577 | 0.778 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.062 | 0.042 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".