Evaluating Radiological Awareness of Carbon Monoxide Poisoning Among Physicians in Saudi Arabia
Bibliographic record
Abstract
BACKGROUND: A poisonous, colorless, odorless, and tasteless gas is carbon monoxide (CO). High-level exposure to CO can result in serious illness or death, and CO poisoning is now recognized as a critical public health concern worldwide. The purpose of this study was to evaluate Saudi Arabian physicians' radiological knowledge of CO intoxication. METHODS: This online survey research, which was cross-sectional, assessed doctors' knowledge and awareness of CO poisoning in Saudi Arabia. Licensed physicians were invited to complete an online questionnaire covering demographics and work data, exposure to CO poisoning cases, training history, radiological awareness, and physicians' practice and attitude towards CO poisoning. The questionnaire was validated for clarity, reliability, and relevance and was shared on social media platforms for participation. RESULTS: Among 393 eligible physicians, 127 (32.3%) had received training on CO poisoning and its radiological manifestations, while 137 (34.9%) had encountered suspected CO poisoning cases in their clinical practice. Overall, 340 (86.5%) physicians demonstrated poor knowledge and awareness of radiological indicators of CO poisoning, while only 53 (13.5%) exhibited adequate awareness. Higher awareness was significantly associated with greater years of experience (p<0.05). CONCLUSIONS: This study reveals that most physicians in Saudi Arabia lack sufficient knowledge of CO poisoning and its radiological indicators. Although about one-third had relevant exposure and training, the perceived training effectiveness was low. Senior physicians displayed greater awareness, and most respondents acknowledged the importance of identifying radiological signs.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".