Interpreting Patient-Reported Outcome Measures in Orthopaedic Surgery
Bibliographic record
Abstract
BACKGROUND: The Consolidated Standards of Reporting Trials (CONSORT) Statement recommends that studies report results beyond p values and include treatment effect(s) and measures of precision (e.g., confidence intervals [CIs]) to facilitate the interpretation of results. The objective of this systematic review was to assess the reporting and interpretation of patient-reported outcome measure (PROM) results in clinical studies from high-impact orthopaedic journals, to determine the proportion of studies that (1) only reported a p value; (2) reported a treatment effect, CI, or minimal clinically important difference (MCID); and (3) offered an interpretation of the results beyond interpreting a p value. METHODS: We included studies from 5 high-impact-factor orthopaedic journals published in 2017 and 2019 that compared at least 2 intervention groups using PROMs. RESULTS: A total of 228 studies were analyzed, including 126 randomized controlled trials, 35 prospective cohort studies, 61 retrospective cohort studies, 1 mixed cohort study, and 5 case-control studies. Seventy-six percent of studies (174) reported p values exclusively to express and interpret between-group differences, and only 22.4% (51) reported a treatment effect (mean difference, mean change, or odds ratio) with 95% CI. Of the 54 studies reporting a treatment effect, 31 interpreted the results using an important threshold (MCID, margin, or Cohen d), but only 3 interpreted the CIs. We found an absolute improvement of 35.5% (95% CI, 20.8% to 48.4%) in the reporting of the MCID between 2017 and 2019. CONCLUSIONS: The majority of interventional studies reporting PROMs do not report CIs around between-group differences in outcome and do not define a clinically meaningful difference. A p value cannot effectively communicate the readiness for implementation in a clinical setting and may be misleading. Thus, reporting requirements should be expanded to require authors to define and provide a rationale for between-group clinically important difference thresholds, and study findings should be communicated by comparing CIs with these thresholds.
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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 | 0.518 | 0.775 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.014 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".