Multiple testing in orthopedic literature: a common problem?
Bibliographic record
Abstract
BACKGROUND: Performing multiple tests in primary research is a frequent subject of discussion. This discussion originates from the fact that when multiple tests are performed, it becomes more likely to reject one of the null hypotheses, conditional on that these hypotheses are true and thus commit a type one error. Several correction methods for multiple testing are available. The primary aim of this study was to assess the quantity of articles published in two highly esteemed orthopedic journals in which multiple testing was performed. The secondary aims were to determine in which percentage of these studies a correction was performed and to assess the risk of committing a type one error if no correction was applied. METHODS: The 2010 annals of two orthopedic journals (A and B) were systematically hand searched by two independent investigators. All articles on original research in which statistics were applied were considered. Eligible publications were reviewed for the use of multiple testing with respect to predetermined criteria. RESULTS: A total of 763 titles were screened and 127 articles were identified and included in the analysis. A median of 15 statistical inference results were reported per publication in both journal A and B. Correction for multiple testing was performed in 15% of the articles published in journal A and in 6% from journal B. The estimated median risk of obtaining at least one significant result for uncorrected studies was calculated to be 54% for both journals. CONCLUSION: This study shows that the risk of false significant findings is considerable and that correcting for multiple testing is only performed in a small percentage of all articles published in the orthopedic literature reviewed.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.184 | 0.364 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.011 |
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; both teacher heads 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".