Lessons learned from a resident-led clinical trial in obstetrics
Bibliographic record
Abstract
BACKGROUND: Completion of a randomised controlled trial is one way by which the resident research requirement can be met in Canadian obstetrics and gynaecology programmes. However, little is known about the specific challenges of performing clinical trials within the specialty, let alone as a resident project. PURPOSE: A resident-led randomised controlled trial comparing two methods of labour induction at term was halted due to insufficient patient enrolment. A structured review of the study design and recruitment process was conducted to identify factors contributing to poor recruitment. METHODS: In addition to completing a literature review and internal review by the research team, we surveyed obstetricians and residents regarding recruitment efforts and barriers to participation. We solicited feedback on trial design and the expectations of clinicians with respect to participation in research studies. RESULTS: Eight obstetricians (67%) and 13 residents (93%) responded to the survey. All were able to identify eligible patients, though only 60% had invited one or more patients to participate during the recruitment period. Failure to consider trial participation and excessive clinical workload were the most commonly cited barriers for clinicians. Resistance to the test intervention was the major barrier to patient participation. Several residents cited a lack of personal incentive to recruit patients as a significant barrier. LIMITATIONS: The research team was unable to contact patients directly, thus limiting the scope of our review to our internal methods and feedback from clinicians. CONCLUSIONS: Poor recruitment in a resident-led clinical trial in obstetrics resulted from multiple coexisting factors. A structured review provided valuable insight for the research team. Academic clinicians and trainees in all specialties should be encouraged to share their experiences in the hope of improving the likelihood of success in future research endeavours.
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How this classification was reachedexpand
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 | 0.084 | 0.961 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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; both teacher heads 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".