Specific skill set and goals of focused echocardiography for critical care clinicians
Bibliographic record
Abstract
Echocardiography in the critical care setting can provide crucial information about the patient's cardiac anatomy, ejection fraction, valvular function, and volume status. There is a need for more involvement by intensivists in performing focused echocardiographic studies as this modality has been well shown to improve patient care. Several factors limit the widespread use of this technology by intensivists that are noncardiologists. One of them is the lack of formal didactic and practical training programs in "goal-directed" echocardiography specifically oriented for the critical care specialist. Although it is clear that extensive training and experience are needed to perform and interpret a complete echocardiographic study, a growing body of literature demonstrates that noncardiology medical professionals can be trained to acquire and interpret echocardiographic imaging in a goal-directed or "focused" manner with an acceptable overall level of accuracy. Performance of such focused echocardiography by intensivists has been shown to provide new information not assessable by physical examination, and often leads to change in therapeutic management at the bedside. Echocardiography using the transthoracic approach is a noninvasive imaging modality and is of great value in the critical care setting because of its portability, widespread availability, and rapid diagnostic capability. Programs for intensivists should cover both the transthoracic and transesophageal approach. Focused training with the transthoracic approach should be offered to all intensivists, while training with the transesophageal approach should be offered to intensivists who desire more advanced training. This article will go over important issues regarding current and potential avenues for training of critical care physicians in performance of focused bedside echocardiography.
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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".