Misuse of Baseline Comparison Tests and Subgroup Analyses in Surgical Trials
Bibliographic record
Abstract
It is unclear whether the misuse of statistical tests that compare patients' baseline characteristics and subgroup analyses in randomized controlled trials can be extrapolated to the surgical literature. We did an observational study evaluating the current use of baseline comparability tests and subgroup analyses in surgical randomized controlled trials. Published surgical randomized controlled trials in four medical journals were identified. We also identified randomized controlled trials in the Journal of Bone and Joint Surgery (American and British volumes). We identified 72 randomized controlled trials, with a mean of 10 +/- 8 baseline variables. Of 166 significance tests, 17 (10%) were significant. Twenty-seven (38%) trials included 54 subgroup analyses with a minimum of one and maximum of 23 subgroup analyses per study. Inappropriate emphasis on subgroup analyses occurred frequently. Forty-nine (91%) analyses were performed post hoc without prior hypotheses. Investigators reported differences between subgroups in 31 (57%) of the analyses, all of which were featured in the summary or conclusion. These inferences may be misleading, making their application to clinical practice unwarranted.
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 | 0.846 | 0.948 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.018 | 0.024 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".