HOW MANY PATIENTS? HOW MANY LIMBS? ANALYSIS OF PATIENTS OR LIMBS IN THE ORTHOPAEDIC LITERATURE
Bibliographic record
Abstract
Background: Clinical studies assessing orthopaedic interventions often include data from two limbs or multiple joints within single individuals. Without appropriate design or statistical approaches to address within-individual correlations, this practice may contribute to false precision and possible bias in estimates of treatment effect. We conducted a systematic review of the orthopaedic literature to determine the frequency of inappropriate inclusion of nonindependent limb or joint observations in clinical studies. Methods: We identified seven orthopaedic journals with high Science Citation Index impact factors and retrieved all clinical studies for 2003 for any intervention on any limb or joint. Results: We identified 288 clinical studies, 143 of which involved two limbs or multiple joint observations from single individuals. These studies included nineteen randomized clinical trials (13%) fifty-eight two-group cohort studies (41%), and sixty-six one-group cohort studies (46%). Seventy-six (53%) of the 143 studies involved statistical comparisons between patient groups with use of tests of association, and an additional sixty studies (42%) presented estimates of proportions without statistical comparisons. Only sixteen of the seventy-six studies involving statistical comparisons involved the use of any technique or methodological approach to account for multiple, nonindependent observations. A median of approximately 13% of the patients in these studies contributed more than one observation. The median proportion of nonindependent observations to total observations (the unit of analysis) was approximately 23%. Conclusions: Our findings suggest that a high proportion (42%) of clinical studies in high-impact-factor orthopaedic journals involve the inappropriate use of multiple observations from single individuals, potentially biasing results. Orthopaedic researchers should attend to this issue when reporting results.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".