Survey of the training and use of echocardiography and lung ultrasound in Australasian intensive care units
Bibliographic record
Abstract
expertise (diagnostic versus focused) was highest in TTE (32 %) compared with TOE (22 %) and LU (12 %). The proportions of intensivists untrained in TTE and LU was 41 % and 30 %, respectively. Perceived barriers included lack of organized training (38 %) and time for training (25 %). Other barriers included a perceived lack of need for training (18 %), insufficient equipment (14 %), and resistance from other ultrasound providers (4 %). The most commonly reported training programs were tertiary courses, such as provided by the Australasian Society of Ultrasound in Medicine (68 %) and University of Melbourne (59 %), rather than board examinations or hands-on workshops. We conclude that although TTE and LU are used frequently in Australasian teaching ICUs, many ICU physicians are yet to be trained due to lack of ICU training programs and time for training. Although tertiary courses are popular and provide training to diagnostic level, they are lengthy and depend on trainers and patient caseload and are not, therefore, scalable. An attractive alternative is to begin training in medical school and to train more physicians in basic ultrasound with shorter, more efficient, and hands-on courses utilizing the internet and ultrasound simulators [1], advancing to a diagnostic level only if required.
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 | 0.000 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".