Identification and diagnosis of patients with familial chylomicronaemia syndrome (FCS): Expert panel recommendations and proposal of an “FCS score”
Bibliographic record
Abstract
Familial chylomicronaemia syndrome (FCS) is a rare, inherited disorder characterised by impaired clearance of triglyceride (TG)-rich lipoproteins from plasma, leading to severe hypertriglyceridaemia (HTG) and a markedly increased risk of acute pancreatitis. It is due to the lack of lipoprotein lipase (LPL) function, resulting from recessive loss of function mutations in the genes coding LPL or its modulators. A large overlap in the phenotype between FCS and multifactorial chylomicronaemia syndrome (MCS) contributes to the inconsistency in how patients are diagnosed and managed worldwide, whereas the incidence of acute hypertriglyceridaemic pancreatitis is more frequent in FCS. A panel of European experts provided guidance on the diagnostic strategy surrounding FCS and proposed an algorithm-based diagnosis tool for identification of these patients, which can be readily translated into practice. Features included in this FCS score comprise: severe elevation of plasma TGs (fasting TG levels >10 mmol/L [885 mg/dL] on multiple occasions), refractory to standard TG-lowering therapies, a young age at onset, the lack of secondary factors (except for pregnancy and oral oestrogens) and a history of episodes of acute pancreatitis. Considering 53 FCS patients from three cohorts and 52 MCS patients from three cohorts, the overall sensitivity of the FCS score (≥10) was 88% (95% confidence interval [CI]: 0.76, 0.97) with an overall specificity of 85% (95% CI: 0.75, 0.94). Receiver operating characteristic curve area was 0.91. Pragmatic clinical scoring, by standardising diagnosis, may help differentiate FCS from MCS, may alleviate the need for systematic genotyping in patients with severe HTG and may help identify high-priority candidates for genotyping.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".