Neurologists' understanding and management of conversion disorder
Bibliographic record
Abstract
BACKGROUND: Conversion disorder is largely managed by neurologists, for whom it presents great challenges to understanding and management. This study aimed to quantify these challenges, examining how neurologists understand conversion disorder, and what they tell their patients. METHODS: A postal survey of all consultant neurologists in the UK registered with the Association of British Neurologists. RESULTS: 349 of 591 practising consultant neurologists completed the survey. They saw conversion disorder commonly. While they endorsed psychological models for conversion, they diagnosed it according to features of the clinical presentation, most importantly inconsistency and abnormal illness behaviour. Most of the respondents saw feigning as entangled with conversion disorder, with a minority seeing one as a variant of the other. They were quite willing to discuss psychological factors as long as the patient was receptive but were generally unwilling to discuss feigning even though they saw it as their responsibility. Those who favoured models in terms of feigning were older, while younger, female neurologists preferred psychological models, believed conversion would one day be understood neurologically and found communicating with their conversion patients easier than it had been in the past. DISCUSSION: Neurologists accept psychological models for conversion disorder but do not employ them in their diagnosis; they do not see conversion as clearly different from feigning. This may be changing as younger, female neurologists endorse psychological views more clearly and find it easier to discuss with their patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".