Point-of-care diagnostic lung ultrasound is highly applicable to the practice of medicine in Saudi Arabia but the current skills gap limits its use
Bibliographic record
Abstract
CONTEXT: Coronavirus disease 2019 (COVID-19) has put a spotlight on point-of-care diagnostic lung ultrasound (POCDLUS). However, the spectra of respiratory disease and resources available for investigation vary internationally. The applicability of POCDLUS to internal medicine (IM) practice in Saudi Arabia and the current use by Saudi physicians are unknown. AIMS: The aim of the present study was to determine the applicability of POCDLUS to IM practice in Saudi Arabia and quantify the residents' current skills, accreditation, and use of POCDLUS. METHODS: A questionnaire was distributed to the IM residents at our institution to assess their knowledge, use of POCDLUS, and their perceptions of its applicability in IM. STATISTICAL ANALYSIS: Standard descriptive statistical techniques were used. Categorical data, presented as frequency, were compared using the Chi-squared test. The Likert scale responses, presented as mean ± standard deviation, were compared with a Student's t-test. RESULTS: = 7) had received training, nine used POCDLUS regularly, none were accredited and the overall self-reported level of knowledge was poor. CONCLUSIONS: Whilst POCDLUS is applicable to IM practice in Saudi Arabia, the significant skills gap preclude the provision of a POCDLUS service. As COVID-19 can cause an interstitial syndrome, our pandemic preparation response should include POCDLUS training. The current study is supported by a similar Canadian study and the international standardisation of POCDLUS training may be feasible. The findings of the current study may facilitate the development of POCDLUS training programs for internists throughout Saudi Arabia.
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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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".