Focused Cardiac Ultrasound is Applicable to Internal Medicine and Critical Care but Skill Gaps Currently Limit Use
Bibliographic record
Abstract
CONTEXT: Coronavirus Disease 2019 (COVID-19) put a spotlight on focused cardiac ultrasound (FoCUS). However, the spectra of cardiac disease, and the resources available for investigation vary internationally. The applicability of FoCUS to internal medicine (IM) and critical care medicine (CCM) practice in Saudi Arabia and their current use of FoCUS are unknown. AIMS: To determine the applicability of FoCUS to IM and CCM practice in Saudi Arabia and quantify the residents' current proficiency, accreditation and use of FoCUS. METHODS: A questionnaire was distributed to the residents in IM and CCM at our institution to determine their proficiency, use of FoCUS, and perceptions of its applicability. RESULTS: In total, 110 residents (IM 100/108; CCM 10/10) participated (Response rate 93.2%) and reported that FoCUS was very applicable to their practice, most specifically for pericardial effusion, right heart strain, and left ventricular function. Two IM residents had received postgraduate training, ten used FoCUS regularly, none were accredited and overall self-reported proficiency was poor. In contrast all CCM residents had received postgraduate training and reported regular use of FoCUS. Two were accredited. CONCLUSIONS: Whilst FoCUS is applicable to IM practice in Saudi Arabia, significant skills gaps exist. The skills gap in CCM is lower but unaccredited practice is common. Our residents' responses were similar to those from Canada. Thus, international standardization of FoCUS training could be considered.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".