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 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 | 0.157 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".