Inconsistencies in the Methodological Framework Throughout Published Studies in High-Impact Orthopaedic Journals
Bibliographic record
Abstract
BACKGROUND: Both the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) and Consolidated Standards of Reporting Trials (CONSORT) guidelines recommend that clinical trials follow a study framework that aligns with their objective to test the relative efficacy or safety (equality) or effectiveness (superiority, noninferiority, or equivalence) between interventions. We conducted a systematic review to assess the proportion of studies that demonstrated inconsistency between the framing of their research question, sample size calculation, and conclusion and those that should have framed their research question differently based on the compared interventions. METHODS: We included studies from 5 high-impact-factor orthopaedic journals published in 2017 and 2019 that compared at least 2 interventions using patient-reported outcome measures. RESULTS: We included 228 studies. The sample size calculation was reported in 60.5% (n = 138) of studies. Of these, 52.2% (n = 72) were inconsistent between the framing of their research question, sample size calculation, and conclusion. The majority (n = 137) of sample size calculations were for equality, but 43.8% of these studies concluded superiority, noninferiority, or equivalence. Studies that framed their research question as equality (n = 186) should have been framed as superiority (n = 129), equivalence (n = 52), or noninferiority (n = 3). Only 2 studies correctly framed their research question as equality. CONCLUSIONS: Studies published in high-impact journals were inconsistent between the framing of their research question, sample size calculation, and conclusion. Authors may be misinterpreting research findings and making clinical recommendations solely based on p values. Researchers are encouraged to state and justify their methodological framework and choice of margin(s) in a publicly published protocol as they have implications for sample size and the applicability of conclusions. CLINICAL RELEVANCE: The results of clinical research must be interpreted using confidence intervals, with careful consideration as to how the confidence intervals relate to clinically meaningful differences in outcomes between treatments. The more typical practice of relying on p values leaves the clinician at high risk of erroneous interpretation, recommendation, and/or action.
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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 | 0.700 | 0.888 |
| Meta-epidemiology (narrow) | 0.002 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.046 | 0.042 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".