An International Survey Comparing Different Physician Models for Health Care Delivery to Critically Ill Children With Heart Disease
Bibliographic record
Abstract
OBJECTIVES: To explore relationships between the training background of cardiac critical care attending physicians and self-reported perceived strengths and weaknesses in their ability to provide clinical care. DESIGN: Cross-sectional observational survey sent worldwide to ~550 practicing cardiac ICU attending physicians. SETTING: Hospitals providing cardiac critical care. SUBJECTS: Practicing cardiac critical care physicians. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We received responses from 243 ICU attending physicians from 82 centers (14 countries). The primary training background of the respondents included critical care (62%), dual training in critical care and cardiology (16%), cardiology (14%), and other (8%). We received 49 responses from medical directors in nine countries, who reported that the predominant training background for attending physicians who provide cardiac intensive care at their institutions were critical care (58%), dual trained (18%), cardiology (12%), and other (11%). A greater proportion of physicians trained in either critical care or dual-training reported feeling confident managing multiple organ failure, neurologic conditions, brain death, cardiac arrest, and performing procedures like advanced airway placement and inserting chest- and abdominal-drains. In contrast, physicians with cardiology and dual-training reported feeling more confident managing intractable arrhythmias, understanding cardiopulmonary interactions, and interpreting echocardiogram, electrocardiogram, and cardiac catheterization. Overall, only 57% of the respondents felt comfortable based on their current training background to manage patients with complex cardiac issues without collaboration with other specialists. CONCLUSIONS: Our survey demonstrates that intensivists trained in critical care are more comfortable with critical care skills, cardiology-trained intensivists are more comfortable with cardiology skills, and dual-trained physicians are comfortable with both critical care skills and cardiology skills. These findings may help inform future efforts to optimize the educational curriculum and training pathways for future cardiac intensivists. These data may also be used to shape continuing medical education activities for cardiac intensivists who have already completed their training.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".