Multidisciplinary treatment for functional neurological symptoms: a prospective study
Bibliographic record
Abstract
Although functional neurological symptoms are often very disabling there is limited information on outcome after treatment. Here we prospectively assessed the short- and long-term efficacy of an inpatient multidisciplinary programme for patients with FNS. We also sought to determine predictors of good outcome by assessing the responsiveness of different scales administered at admission, discharge and follow-up. Sixty-six consecutive patients were included. Assessments at admission, discharge and at 1 year follow-up (55%) included: the Health of the Nation Outcome Scale, the Hospital Anxiety and Depression Scale, the Patient Health Questionnaire-15, the Revised Illness Perception Questionnaire, the Common Neurological Symptom Questionnaire, the Fear Questionnaire and the Canadian Occupational Performance Measure. At discharge and at 1 year follow-up patients were also asked to complete five-point self-rated scales of improvement. There were significant improvements in clinician-rated mental health and functional ability. In addition, patients reported that their levels of mood and anxiety had improved and that they were less bothered by somatic symptoms in general and neurological symptoms in particular. Two-thirds of patients rated their general health such as "better" or "much better" at discharge and this improvement was maintained over the following year. Change in HoNOS score was the only measure that successfully predicted patient-rated improvement. Our data suggest that a specialized multidisciplinary inpatient programme for FNS can provide long-lasting benefits in the majority of patients. Good outcome at discharge was exclusively predicted by improvement in the HoNOS which continued to improve over the 1 year following discharge.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".