P.058 Neuromuscular neurologists’ experience in recognizing, diagnosing, and treating Long-chain fatty acid disorders (LC-FAOD): a national survey
Bibliographic record
Abstract
Background: LC-FAOD may be missed in neuromuscular (NM) clinics due to its rarity and absence from common NM genetic panels. The Canadian Neuromuscular Disease Registry (CNDR) collects real-world patient data and includes a network of clinician-investigators. Our objective was to inform future registry work by evaluating diagnosis pathways for LC-FAOD patients and estimating the number followed at Canadian NM clinics. Methods: A questionnaire was developed with an expert committee and circulated to 111 CNDR-affiliated NM neurologists. Results: 12 neurologists in 5 provinces, primarily adult-treating (n=8) completed the survey (10.8% response rate). Eleven (91.7%) practiced for >10 years. Agreement trends existed between definition of, and tests to evaluate, rhabdomyolysis. Four clinics routinely follow LC-FAOD patients. In the last 1-2 years, respondents diagnosed approximately 91 patients with LC-FAOD (mean=7.5 per clinic). 83.3% never received continuing education on LC-FAOD, though 75% indicated interest in expert-led webinars. Further data will be presented. Conclusions: Low sample size limits conclusions about LC-FAOD clinical trends. Results suggest LC-FAOD may be under-diagnosed or not routinely followed by NM specialists, limiting viability of an LC-FAOD registry. Practitioners may be interested in LC-FAOD-specific education. Future work could include collaboration with metabolic geneticists on education initiatives to raise awareness and improve care for these patients.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".