Clinical management guidelines for Friedreich ataxia: best practice in rare diseases
Bibliographic record
Abstract
BACKGROUND: Individuals with Friedreich ataxia (FRDA) can find it difficult to access specialized clinical care. To facilitate best practice in delivering healthcare for FRDA, clinical management guidelines (CMGs) were developed in 2014. However, the lack of high-certainty evidence and the inadequacy of accepted metrics to measure health status continues to present challenges in FRDA and other rare diseases. To overcome these challenges, the Grading of Recommendations Assessment and Evaluation (GRADE) framework for rare diseases developed by the RARE-Bestpractices Working Group was adopted to update the clinical guidelines for FRDA. This approach incorporates additional strategies to the GRADE framework to support the strength of recommendations, such as review of literature in similar conditions, the systematic collection of expert opinion and patient perceptions, and use of natural history data. METHODS: A panel representing international clinical experts, stakeholders and consumer groups provided oversight to guideline development within the GRADE framework. Invited expert authors generated the Patient, Intervention, Comparison, Outcome (PICO) questions to guide the literature search (2014 to June 2020). Evidence profiles in tandem with feedback from individuals living with FRDA, natural history registry data and expert clinical observations contributed to the final recommendations. Authors also developed best practice statements for clinical care points that were considered self-evident or were not amenable to the GRADE process. RESULTS: Seventy clinical experts contributed to fifteen topic-specific chapters with clinical recommendations and/or best practice statements. New topics since 2014 include emergency medicine, digital and assistive technologies and a stand-alone section on mental health. Evidence was evaluated according to GRADE criteria and 130 new recommendations and 95 best practice statements were generated. DISCUSSION AND CONCLUSION: Evidence-based CMGs are required to ensure the best clinical care for people with FRDA. Adopting the GRADE rare-disease framework enabled the development of higher quality CMGs for FRDA and allows individual topics to be updated as new evidence emerges. While the primary goal of these guidelines is better outcomes for people living with FRDA, the process of developing the guidelines may also help inform the development of clinical guidelines in other rare diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".