How Many Patients Do We Need? Predictors of Consent to Participate in Clinical Research Studies in Orthopaedic Trauma
Bibliographic record
Abstract
OBJECTIVES: To characterize the recruitment rates at a Level I trauma center enroling for multiple prospective orthopaedic trauma research studies and identify patient-related and study-related predictors of consent. DESIGN: We conducted a case-control study to identify predictors of study consent. The authors categorized studies based on intensity of the study intervention (low, intermediate, or high). A 2-level generalized linear model with random intercept for study was used to predict study consent. SETTING: This analysis includes data from 10 federally funded studies conducted as part of a large, national consortium that were enroling patients in 2013-2014. PATIENTS/PARTICIPANTS: Three hundred thirty-four patients were approached for at least 1 study and included in the analysis. INTERVENTION: N/A. MAIN OUTCOME MEASURES: Consent to participate in the research study. RESULTS: A total of 315 patients consented to be in a study (71% of approached patients). Consent rate varied by study (45%-95%). No patient characteristics (race, age, or sex) were associated with consent. Patients approached for studies of intermediate intensity were 83% less likely to consent (odds ratio = 0.17; 95% confidence interval: 0.04-0.67), and those approached for studies of high intensity were 91% less likely to consent (odds ratio = 0.09; 95% confidence interval: 0.03-0.32). CONCLUSION: Patient factors were not associated with consent. Study intensity is a major driver of consent rates. Studies of higher intensity will require the study team to approach up to twice as many patients as the target enrolment. This study provides a framework that can be used in study planning and determination of feasibility.
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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 | 0.027 | 0.213 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".