The Risk of False-Positive Results in Orthopaedic Surgical Trials
Bibliographic record
Abstract
The risk of concluding that the results of a particular study are true, when, in fact, they really are attributable to chance (or random sampling error) is underappreciated by investigators. This erroneous false-positive conclusion is designated as a Type I or alpha error. The extent to which randomized trials in surgery risk Type I errors is unclear. The current authors hand-searched four orthopaedic journals, six general surgery journals, and five medical journals to identify recently published randomized trials (within the past 2 years). Information on outcomes and statistical adjustment for multiple outcomes was recorded for each study. The risk of a Type I error was calculated for each study that did not explicitly state a primary outcome measure for the main statistical comparison. One hundred fifty-nine studies met the inclusion criteria for the study: 60 studies from orthopaedic journals, 49 studies from nonorthopaedic surgical journals, and 50 studies from medical journals. Of the trials that did not state a primary outcome measure, the risk of Type I errors (false-positive results) in orthopaedic and nonorthopaedic surgery journals (mean 37.3% +/- 13.3% and 37.6% +/- 10.5%, respectively) were significantly greater than medical journals (10.1% +/- 1.9%). In the current review of randomized trials in surgery and medicine, the following is reported: (1) reporting of primary outcomes in trials was inadequate; (2) one in three trials in surgery and one in 10 trials in medicine risked false-positive results; and (3) few trials in surgery and medicine considered adjustment for multiple comparisons.
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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 | 0.769 | 0.900 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".