The head nodding syndrome—Clinical classification and possible causes
Bibliographic record
Abstract
PURPOSE: In the 1960s in Tanzania, L. Jilek-Aall observed a seizure disorder characterized by head nodding (HN). Decades later, "nodding disease," reminiscent of what was seen in Tanzania, was reported from Sudan. To date this seizure disorder has not been classified and possible causes still remain obscure. METHODS: In a prospective study in southern Tanzania, we evaluated 62 patients with HN. Selected patients underwent blood (n = 51) and cerebrospinal fluid (CSF) (n = 48) analyses. Others were chosen for MRI (n = 12) and EEG (n = 10). RESULTS: Seizure type was classified as "head nodding only" and "head nodding plus," the latter being combined with other types of seizure (n =34). During HN, consciousness was impaired in 11 patients (17.7%) and supportive signs of epileptic seizures were described by 15 (24.2%) patients. Precipitating factors were confirmed by 11 (17.7%) patients. Fifty-six (90.3%) patients had at least one relative with epilepsy. EEG confirmed interictal epileptic activity in two patients and unspecific changes in four patients. MRI showed hippocampus pathologies (n = 5) and gliotic changes (n = 5). Skin polymerase chain reaction (PCR) positivity for Onchocerca volvulus was significantly associated with lesions on MRI. However, PCR of the CSF was negative in all cases. CONCLUSIONS: We present a comprehensive clinical description of the "HN syndrome," possibly a new epilepsy disorder in sub-Saharan Africa. MRI lesions and their association with positive skin PCR for O. volvulus despite negative PCR of the CSF is intriguing and deserves attention. Furthermore, the high prevalence of hippocampus sclerosis and familial clustering of epilepsy may point toward other potential pathogenetic mechanisms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".