Users' guide to the surgical literature. Case-control studies in surgical journals.
Bibliographic record
Abstract
INTRODUCTION: Some articles in surgical journals identify themselves as case-control studies, but their methods differ substantially from conventional epidemiologic case-control study (ECC) designs. Most of these studies appear instead to be retrospective cohort studies or comparisons of case series. METHODS: We identified all self-identified "case-control" studies published between 1995 and 2000 in 6 surgical journals, to determine the proportion that were true ECCs and to identify study characteristics associated with being true ECCs. RESULTS: Only 19 out of 55 articles (35%) described true ECCs. More likely to be ECCs were those articles that reported "odds ratios" (ORs) (the OR for being an ECC if a study reported "ORs" compared with those reporting no "ORs" 15.3; 95% confidence interval [CI] 2.8-82.6) and whose methods included logistic regression analysis (OR 3.6, CI 1.0-12.9). Studies that focused on the evaluation of a surgical procedure were less likely to be ECCs (OR 0.2, CI 0.1-0.7) than other types of studies, such as those focusing on risk factors for disease. CONCLUSIONS: The term "case-control study" is frequently misused in the surgical literature.
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 | 0.013 | 0.088 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.043 | 0.034 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.430 | 0.255 |
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".