Cardiovascular Critical Care
Bibliographic record
Abstract
OBJECTIVE: Acute and chronic cardiovascular comorbidities are common among critically ill individuals. It is unclear if current critical care fellowship trainees feel adequately prepared to manage these conditions. DESIGN: Prospective, cross-sectional survey. PATIENTS OR SUBJECTS: Trainees enrolled in U.S. critical care training programs. SETTING: Accredited pulmonary/critical care, surgery/critical care, anesthesiology/critical care, and stand-alone critical care training programs. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A 19-item survey assessing trainee confidence in the management of cardiac critical illness and the performance of cardiac-specific critical care interventions was constructed using Accreditation Council for Graduate Medical Education recommendations as a reference. After validation, the survey was electronically sent to all training programs for dissemination to their trainees. Confidence scores were measured on a Likert scale from 1 to 5. A total of 134 completed surveys were analyzed. Overall, respondents reported lower confidence in managing cardiovascular compared with noncardiovascular diseases in the ICU (4.0 vs 4.6 out of 5). Likewise, they reported lower perceived competence in performing cardiovascular procedures specific to the ICU (2.9 vs 4.5 out of 5). The majority (88%) of those surveyed felt that they would benefit from increased didactic and clinical experience in the management of cardiovascular critical illness. CONCLUSIONS: Current critical care fellows may be unprepared to deal with the increasing prevalence of cardiovascular illness in the ICU. This potential educational gap warrants timely attention to ensure that future graduates have the requisite skills necessary to manage these critically ill patients and presents a unique opportunity to develop multidisciplinary partnerships for enhancing training.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.160 | 0.047 |
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".