Conducting Prospective Research as a Trainee: Experiences with the DRIVE-SAFE Study
Bibliographic record
Abstract
Conducting clinical research during a 2-year critical care fellowship is a challenging endeavor. Fellows are often met with multiple barriers when considering clinical research projects during fellowship, including time, mentorship, resources, and clinical support. This paper presents the perspective and experiences of a group of critical care fellows who conducted the DRIVE-SAFE (Driving Pressure in Assisted Ventilation as a Predictor for Successful Liberation from Invasive Mechanical Ventilation) feasibility study, which aimed to determine measurable physiological variables that could be associated with lung injury and affect duration of mechanical ventilation. This paper provides a guide for trainees on how to conduct prospective clinical research at the bedside. We describe three key steps, including formulating a research question, developing appropriate methodology, and establishing outcomes. We also present the challenges that trainees may encounter when conducting prospective studies and how to overcome these challenges with proper mentorship, training, and collaboration with key stakeholders. These perspectives may provide useful guidance for current and future trainees interested in conducting prospective clinical research at the bedside.
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".