Opinions and experience of neurologists and neurology trainees in Saudi Arabia on functional neurological disorders: a survey based study
Bibliographic record
Abstract
BACKGROUND: Functional Neurological Disorder (FND) is commonly encountered in clinical practice, causing functional impairment and poor quality of life. As there is limited data from Saudi Arabia, our study aims to explore the experience and opinions of Saudi neurologists and neurology trainees regarding FND. METHODS: In our cross-sectional observational study, we included 100 neurology consultants and trainees. Data was collected using an online questionnaire from March to August 2023. RESULTS: A total of one hundred neurologists participated in the survey. Although 41% of physicians encountered FND patients on a weekly basis or more frequently, only 41.7% of trainees reported receiving dedicated lectures on FND. Furthermore, only 46% of respondents felt comfortable providing a clear explanation of the FND diagnosis to their patients. While the majority (64%) used the term "Functional Neurological Disorder" in medical documentation, only 43% used this term when communicating the diagnosis to patients, with the terminology varying widely. Clinicians emphasized that inconsistent and variable neurological examinations were key indicators raising diagnostic suspicion, which aligns with the recommended reliance on detailed clinical history and neurological examination. Lastly, 61% of physicians stated that their approach to patients with FND lacked a structured management plan. CONCLUSION: Our study findings emphasize that FND is commonly encountered in clinical practice and reveal a significant lack of targeted education on FND for neurology trainees. Enhancing educational programs for both trainees and practicing neurologists on this prevalent neurological condition is essential for improving patient care and outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".