Factors Influencing the Enrollment of Eligible Individuals in Orthopedic Randomized Controlled Trials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it