TETRAS Spirals and Handwriting Samples: Determination of Optimal Scoring Examples
Bibliographic record
Abstract
Background: Spiral drawings and handwriting tasks have long been used to assess the severity of essential tremor, but these motor tasks are somewhat less objective as the rules for scoring are not based on firm objective amplitude-based criteria. Publishing the best examples of each of the possible 0-4 ratings for these items could reduce scoring variance. Methods: 21 members of the Tremor Research Group each rated 94 spirals and 64 handwriting samples using TETRAS scoring criteria. For each sample, the most frequently reported score (mode; maximum of 21) was determined. Ratings not adjacent to the mode were subtracted from the number of mode scores, to calculate a total value. For each of the ratings (0, 1, 1.5, 2, 2.5, 3, 3.5, 4), the samples with the highest total value were selected as best examples. Results: In general, rater agreement was good for spirals but poor for handwriting samples. Nevertheless, examples with excellent agreement were identified for all spiral and handwriting ratings, and are presented. Conclusion: Best examples for scoring spirals and handwritings are needed to reduce the variance of TETRAS scores in clinical trials and clinical practice.
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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 | 0.036 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".