Bibliographic record
Abstract
BACKGROUND: It has been suggested that patients with migraine are at a higher risk of developing essential tremor (ET). In addition, it was shown that patients with migraine are at higher risk of subclinical vascular infarcts in the cerebellum, a structure believed to be implicated in ET. OBJECTIVE: Determine whether patients with migraine who do not show clinically detectable ET have subtle alteration in their physiological tremor characteristics that may serve as a predictor for the eventual appearance of ET. METHODS: The physiological tremor of 30 patients with migraine (25 women, mean age: 41 +/- 8 years) was examined using a laser displacement sensor. Tremor was recorded in 5 conditions: hand-rest, hand-postural, finger-rest, finger-postural, and finger-loading (70 g). We also recorded tremor in healthy controls who never experienced migraine. Amplitude, median power frequency, power dispersion (width of a frequency band containing 68% of the power), and power distribution within 3 predetermined frequency bands of interest (3.5 to 7.5, 7.5 to 12.5, and 16 to 30 Hz) were assessed in each condition. RESULTS: All tremor characteristics described above were very similar between the migraine group and controls. These results were supported by the lack of correlation between tremor characteristics and the number of years of experiencing migraine (ranging from 3 to 41 years; mean: 20 +/- 10). Patients with aura (N = 21) had tremor characteristics similar to that of patients without aura (N = 9) and controls. CONCLUSION: These results suggest that, if a link exists between migraine and ET, the latter might be the result of an "acute event" (eg, stroke) rather than a progressive alteration of tremorogenic 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".