MISINTERPRETATION OF “NEGATIVE” RESULTS OF SUPERIORITY TRIALS IN ORTHOPAEDIC LITERATURE: THE NEED FOR NON-INFERIORITY TRIALS
Bibliographic record
Abstract
A commonly misunderstood principle in medical literature is statistical significance. Often, statistically non-significant or negative results are thought to be evidence for equivalence; mistakenly validating treatment modalities and putting patients at risk. This study examines the prevalence of misinterpretation of negative results of superiority trials in orthopaedic literature and outlines the need for a non-inferiority or equivalence research design. Four orthopaedic journals – Journal of Paediatric Orthopaedics A, Journal of Bone and Joint Surgery American Volume, Journal of Arthroplasty and Journal of Shoulder and Elbow Surgery – were hand searched to identify all randomised control trials (RCTs) published within the time periods 2002–2003, 2007–2008 and 2012–2013. The identified RCTs were read and classified by study methodology, results obtained, and interpretation of results. A total of 237 RCTs were identified. When analysing the primary outcomes, 117 (49.4%) studies yielded negative results and 120 (50.8%) yielded positive results. Out of the 237 articles, 231 (97.5%) used superiority methodology and 6 (2.5%) used non-inferiority or equivalence methodology. Of the 231 studies that used superiority methodology, 115 (49.8%) obtained negative results; and 45 (39.1%) of those misinterpreted the negative results for equivalence. While no statistical differences were seen, there was an upward trend in utilising non-inferiority and equivalence methodologies over time. Given the frequency of misinterpreted negative results, there is an evident need for a more appropriate research methodology that shows equivalence of treatment methods. A non-inferiority or equivalence study design can address orthopaedic clinical dilemmas more suitably when trying to show one treatment is no worse or is equal to another treatment. Regarding orthopaedic treatment modalities as equivalent when studies show negative statistical results can be detrimental to patients and their clinical outcomes. A non-inferiority methodology can be used to accurately depict no difference between treatment methods rather than attempting to show one treatment method as superior.
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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 | 0.911 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.029 | 0.013 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.023 | 0.031 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".