Brief communication: Strong concordance of the North American Familial Chylomicronemia Syndrome Score with a positive genetic diagnosis in patients from the Balance study
Bibliographic record
Abstract
BACKGROUND: Patients with familial chylomicronemia syndrome (FCS) are often misdiagnosed. A positive genetic diagnosis is considered definitive, but clinical scoring systems can also identify affected patients. The North American FCS (NAFCS) Score is intended to identify patients likely to have positive DNA testing, but its sensitivity has not been quantified. OBJECTIVE: To evaluate NAFCS Scores in patients from the Balance study. METHODS: We calculated NAFCS Scores in 66 patients with genetically confirmed FCS from the Balance study of olezarsen. RESULTS: We found that 95.5% (63/66) and 74.2% (49/66) of patients had NAFCS Scores ≥45 ("likely FCS") and ≥60 ("definite FCS"), respectively. In contrast, no patient had a score <30 ("unlikely FCS"), while 4.5% (3/66) had scores between 30 and 44 ("uncertain FCS"). CONCLUSION: The strong concordance between NAFCS Score ≥45 and a positive genetic diagnosis of FCS suggests that either approach can be used for diagnosis except in "uncertain FCS" cases, which require genetic testing. The score might also clinically support an FCS diagnosis when genetic testing is indeterminate due to variants of unknown significance.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".