Measurement of the Applicability of Abdominal Point-of-Care Ultrasound to the Practice of Medicine in Saudi Arabia and the Current Skill Gaps
Bibliographic record
Abstract
Background: Renal, gastrointestinal, and hepatic pathology, and the resources available for their management vary internationally. Whilst abdominal point-of-care ultrasound (APOCUS) should enhance management, uptake by physicians, worldwide, has been poor. So, the aim of this study was to explore the applicability of APOCUS to medical practice in Saudi Arabia, residents’ current ability to perform APOCUS, and the skill gaps. Methods: A validated questionnaire was distributed to the internal medicine residents at our institution to determine their ability to perform APOCUS (self-reported), and obtain their opinions on its applicability for the detection of hepatomegaly, splenomegaly, hydronephrosis, and ascites. Statistical analysis: Standard descriptive statistical techniques were used. Categorical data, presented as frequency, were compared using the χ2 test. The Likert scale responses, presented as mean ± standard deviation, were compared with a t test or analysis of variance. Results: Ninety-eight residents participated (response rate 90.7%). Abdominal POCUS is very applicable to their practice. The use of APOCUS to detect ascites was the most applicable (mean 4.61 ± SD 0.69). However, proficiency in APOCUS was poor (mean 1.65 ± SD 1.11). Conclusions: The difference between internists’ self-reported ability to perform APOCUS and its perceived usefulness demonstrates a skill gap. Thus, whilst APOCUS is applicable to medical practice in Saudi Arabia, significant skill gaps exist.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".