Factors Influencing the Enrollment of Eligible Individuals in Orthopedic Randomized Controlled Trials
Bibliographic record
Abstract
Purpose: Low rates of subject enrollment are a threat to the external validity of clinical trials, which are necessary to confirm or contradict basic assumptions about clinical management. Our goal was to examine the association of subject enrollment rates in orthopedic randomized controlled trials (RCTs) with characteristics of the interventions being studied, the investigators of the studies, and the publications in which the RCTs are reported.\nMethods: We performed a search in PubMed/MEDLINE for RCTs involving an orthopedic surgical procedure, comparing different intraoperative interventions, published in English in a peer-reviewed journal during 2003 to 2014, and reporting both the numbers of enrolled and eligible subjects. The primary outcome variable was the enrollment rate, calculated as the number of enrolled subjects divided by the number of eligible subjects. We collected and analyzed data from papers meeting inclusion criteria.\nResults: The average enrollment rate across all 393 studies meeting inclusion criteria was 84.5% (standard deviation (SD) 16.6%). Trials in the United States and Canada had significantly lower enrollment rates when compared to trials in the rest of the world (72.9% vs. 87.6%, p<0.0001), and trials comparing an operative arm to a non-operative arm had significantly lower enrollment rates than trials comparing two different operative arms (73.1% vs. 86.3%, p<0.0001). The national differences were observed primarily in trials comparing operative and non-operative interventions, in which the average North American enrollment rate was 47.9% (SD 25.9%) and the average enrollment rate elsewhere was 81.1% (SD 15.8%).\nConclusions: Trials may have variable rates of success recruiting subjects depending on their location and the difference between the interventions being studied, with North American trials and trials comparing operative and non-operative interventions having lower enrollment rates.
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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 | 0.735 | 0.828 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".