Development and validation of clinical criteria to identify familial chylomicronemia syndrome (FCS) in North America
Bibliographic record
Abstract
BACKGROUND: Familial chylomicronemia syndrome (FCS) is an ultrarare inherited disorder. Genetic testing is not always feasible or conclusive. European clinicians developed a "FCS score" to differentiate between FCS and multifactorial chylomicronemia syndrome (MCS), a more common condition with overlapping features. A diagnostic score has not been developed for use in the North American (NA) context. OBJECTIVE: To develop and validate a diagnostic score for NA patients based on signs, symptoms and biochemical traits of FCS. METHODS: Using the RAND/UCLA modified Delphi process, we convened 10 US/Canadian physicians with experience recognizing and treating FCS and 1 adult patient with FCS. The panel developed and rated 296 scenarios describing patients with FCS. Linear regression analyses used median post-meeting ratings to develop score parameters. We tested the score's sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) in patients with classical FCS, functional FCS, and MCS from Western University's Lipid Genetics Clinic's registry. RESULTS: Numerical scores were attributed based upon the following: age, hypertriglyceridemia onset, body mass index, history of abdominal pain/pancreatitis, presence of secondary factors, triglyceride (TG) levels, ratio of TG/total cholesterol, and apolipoprotein B level. Scores ≥ 60 indicate definite classical FCS; the score distinguished patients with FCS from MCS in a real-world registry (100.0% specificity, 66.7% sensitivity, 100.0% PPV, 95.5% NPV). Scores ≥ 45 were "very likely" to have classical FCS (96.9% specificity, 88.9% sensitivity). CONCLUSION: Given its simplicity and high specificity for distinguishing patients with FCS from MCS, the NAFCS Score could be used in lieu of - or while awaiting - genetic testing to optimize treatment.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".