Soft signs in movement disorders: friends or foes?
Bibliographic record
Abstract
In movement disorders, emphasis on pure phenomenology to make diagnoses can lead to significant variability and diagnostic disagreement.1 2 This is illustrated by difficulty among experts in deciding whether possibly equivocal signs (‘soft’ signs) are present or absent. A tight pen grip, slightly asymmetric arm swing, or hyperextended fingers may be judged by some to be within the broad spectrum of normal. Others may consider such signs to be pathological. Hence, the relevance of such signs can be uncertain. Furthermore, we are often asked to judge on the presence and clinical relevance of soft signs in ‘neurologically healthy’ individuals and this can be challenging (eg, in clinical genetic studies). Although classification systems tend to include clinical signs to define boundaries of disease, the relevance of soft signs may be questionable if inter-rater reliability is variable. Initially introduced in the psychiatric literature,3 the term ‘soft signs’ has also recently been applied to movement disorders. In the recent Consensus Statement on the Classification of Tremor, the detection of soft signs has, for the first time, become an integral part of tremor classification.4 Because of the uncertainty surrounding the interpretation of soft signs, we asked movement disorders experts (MDE) and experts in fields other than movement disorders (non-MDE) to rate videos of patients and healthy control subjects to assess inter-rater reliability on the presence or absence of soft signs. We asked seven MDE (AJE, AEL, TL, DM, FM, NPQ, MV) and six non-MDE (listed in the Acknowledgements section) to rate 30 videos for the presence or absence of soft signs. Raters were advised that videos may feature control subjects or patients, but no other clinical information was provided. Twenty-five control subjects were recorded (9 …
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".