Appropriateness of Treatments for Patients With Degenerative Ataxias: Recommendations by a Panel of Experts
Bibliographic record
Abstract
Background: The a vailable scientific evidence regarding treatment of degenerative ataxias is scarce. Appropriateness of therapy options for degenerative ataxias were evaluated by using the RAND/UCLA Method. Methods: After a systematic literature review, a list of clinical scenarios were developed to simulate situations most likely to arise in clinical practice. An 8-members expert panel rated, in a two rounds process, the appropriateness of each clinical scenario-treatment combination or indication. Analysis used the ratings to categorize each indication as appropriate, of uncertain appropriateness or inappropriate. Results: Final rankings for the indications were as follow: 26/154 (18.3%) appropriate, 36/154 (25.4%) uncertain and 92/154 (56.3%) inappropriate. The agreement rate was 66.2%. For patients with Friedreich ataxia, physostigmine, 5-hydroxytryptophan and amantadine were rated inappropriate while L -carnitine was rated appropriate only for asymptomatic patients or for patients with gait ataxia. Panelists recommended idebenone therapy for Friedreich ataxia complicated by cardiomyopathy . Therapy with 4-aminopyridine was rated inappropriate for episodic ataxia type 1 but it was rated appropriate for type 2. In the treatment of other ataxias, such as autosomal dominant ataxias and the autosomal recessive ataxias not Friedreich, physostigmine, acetazolamide and L - carnitine were rated inappropriate while amantadine was inappropriate only in patients without gait ataxia. All other combinations were considered uncertain. Conclusions: Within the limits of expert opinion, these guidelines provide direction for some common clinical uncertainties in the treatment of degenerative ataxias. doi: http://dx.doi.org/10.4021/jnr122e
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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 | 0.120 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".