Preliminary Predictors of Initial Attendance, Symptom Burden, and Motor Subtype in a US Functional Neurological Disorders Clinic Population
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Although many patients present with functional neurological symptoms (FNS), few US clinics offer specialized FNS care, and data on clinic attendees remain limited. We determined predictors of initial attendance, symptom burden, and FNS subtype in the first patients referred to our Functional Neurological Disorders Clinic for suspected FNS. METHODS: We reviewed the charts of 62 consecutive patients (46 women, 16 men). Regression analyses investigated predictors of keeping the first scheduled clinic appointment. For the 49 patients who did keep that appointment, regression analyses examined neuropsychiatric factors associated with symptom burden and motor FNS subtypes. RESULTS: The odds of not keeping the first appointment were 10.4 times greater for patients referred from the emergency department than from other sources. The patients who kept their appointment reported a symptom burden that was significantly associated with a past FNS-related emergency department visit and a diagnosis of another medically unexplained somatic syndrome. The number of FNS findings on neurological examination also correlated with a history of an FNS-related emergency department visit. Patients with psychogenic non-epileptic seizures reported cognitive complaints and prior psychiatric hospitalizations significantly more often than did patients with other FNS. One fourth of all patients had two or more motor FNS. CONCLUSIONS: In our FNS cohort, patients were less likely to keep an initial clinic appointment if they were referred from the emergency department than from other sources. Patients with psychogenic non-epileptic seizures were more likely to report cognitive symptoms and past psychiatric hospitalizations than patients with other FNS.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".