Revisiting the assessment of tremor: clinical review
Bibliographic record
Abstract
Tremor, an involuntary, rhythmic, and oscillatory movement of a body part, is a frequent presenting symptom to general practice and by far the most common movement disorder presentation, impacting up to 15% of such cases.1 A common initial pattern is symmetric upper-limb involvement during posture and action. Although patients are often worried about Parkinson’s disease (PD), PD tremor usually has easily recognisable features.2 This concern tends to lead to frequent referrals for specialist input despite an alternative diagnosis being more likely in a majority of cases. Essential tremor (ET) is the most common diagnosis given to patients with this presentation, which is estimated to affect 0.4–6.0% of the general population.3 This may be an overestimate as the rubric of ET and the relationship between clinical features and underlying pathophysiology is uncertain. These aspects also potentially contribute to variable diagnostic and treatment outcomes.4 A recent Movement Disorder Society consensus on phenotyping charts a course towards more precise classification.5 This will not only be useful for research but also help in clarifying common clinical syndromes seen in everyday practice. This article outlines an approach to upper-limb tremor presentations in adult patients, developing a previously proposed three cardinal question method for neck pain6 while highlighting salient aspects of the consensus statement that could potentially aid in clinical stratification of cases.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".