Unlocking Coma Assessments: Exploring Healthcare Professionals' Knowledge and Perception of the Full Outline of Unresponsiveness (FOUR) Score in Saudi Arabia
Bibliographic record
Abstract
Background Coma scales play a critical role in assessing the consciousness level of comatose patients, guiding clinical decisions, and predicting patient outcomes. Although the Glasgow Coma Scale (GCS) has been the standard for decades, the Full Outline of UnResponsiveness (FOUR) score offers a more comprehensive assessment. In this study, the awareness, knowledge, and utilization of the FOUR scores among healthcare professionals in Saudi Arabia were explored. Methods This multisite, cross-sectional study was conducted between January and April 2023 and involved physicians specializing in emergency medicine, neurology, neurosurgery, or intensive care. Participants completed a self-administered questionnaire. Results Among 335 participating physicians, only 33% (111) reported having prior knowledge of the FOUR score; 54% (60) of physicians in this group rarely or never used the FOUR score, largely owing to the perception that the GCS suffices (45%, 61), and a lack of awareness among other healthcare professionals (43%, 58). A significant proportion of physicians unfamiliar with the FOUR score have expressed a willingness to adopt alternative scoring systems, and 67% (148) were open to using a system evaluating brainstem reflexes. For respiration and intubation, 65% (143) and 85% (187) of the physicians were open to alternative scoring systems, respectively. There was a significant difference in knowledge between specialties, level of training, and previous neurocritical training (p-values <0.001, 0.032, <0.001, respectively). Conclusion This study revealed a notable gap in knowledge and utilization of the FOUR score in Saudi Arabia, a willingness to explore alternative systems for assessing consciousness, and an interest in comparative studies of various coma scales. Efforts to improve education about the FOUR score among relevant healthcare professionals in Saudi Arabia, in addition to exploring alternative systems, is suggested.
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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 | 0.003 |
| 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 | 0.001 | 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".