Functional neurological disorders after COVID-19 and SARS-CoV-2 vaccines: a national multicentre observational study
Bibliographic record
Abstract
Functional neurological disorders (FNDs) are a common cause of neurology consultations.1 Dissociative seizures, motor and cognitive disorders are the main phenotypes. Diagnosis is made on positive terms: signs of inconsistency, incongruence and variability of physical signs with attention on clinical examination.1 Abnormal emotional processing and expectations are involved in the genesis and perpetuation of FND.1 \n \nFrom the onset of SARS-CoV-2 pandemic, world population has been affected by high levels of stress, uncertainty and misleading information, with a potential impact on mental health. Different studies have ascertained an increase of FND consultations (threefold in an emergency department).2 On the development of SARS-CoV-2 vaccines, several cases of FND following vaccination were published, as well as an official warning from the Functional Neurological Society.3 Post-COVID-19 symptoms (known as Long-COVID-19) have also become a frequent reason for neurology consultation.4 Somatic symptom disorder may be common in these patients, and socioeconomic implications are vast.5 \n \n \nOur experience is that a proportion of patients with FND describe an association with COVID-19 infection/vaccination. Here, we report a cohort of patients with FND for whom COVID-19 or SARS-CoV-2 vaccines were the main precipitant factors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".