Bibliographic record
Abstract
BACKGROUND: Essential Tremor (ET) is one of the most common movement disorders but many controversies still exist in regards to its definition and pathophysiology. In view of the recent published criteria by the Tremor Task Force of the International Parkinson's and Movement Disorders Society (IPMDS), we intended to analyze if this has changed our view of ET and if new developments have arisen since. METHODS: A Medline search for English-written articles was done on June 15, 2019 using the keyword "Essential Tremor". Publications from November 2017 (publication date of the new tremor classification) were taken into account. Reviews, letters and original studies relevant to the subject were selected and reviewed according to the following themes: clinical characteristics, epidemiology, genetics, pathology, biomarkers and treatment. RESULTS: Out of 132 publications the most relevant articles were selected and reviewed (total of 65 articles). The great majority of these studies focused on surgical treatments (new targets, new technologies) while relatively few articles addressed epidemiology, pathology and pathophysiology. CONCLUSIONS: The use of the new classification is not commonly used still, excepting more recent studies on therapeutics. This is in keeping with diverse opinions and criticisms reported by the IPMDS task force members themselves. One important change has been validating ET as a heterogeneous condition and defining the ET-plus category. We propose a further sub-group classification derived from the new definition of ET-plus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".