Imputation of untreated LDL-C in treated subjects with homozygous familial hypercholesterolaemia: An international collaboration
Bibliographic record
Abstract
BACKGROUND AND AIMS: Diagnosis of Homozygous Familial Hypercholesterolaemia (HoFH) relies on untreated low-density lipoprotein-cholesterol (LDL-C) which is often unknown. We determine whether untreated LDL-C can be imputed from treated LDL-C in HoFH. METHODS: Two groups with HoFH were identified: Group 1 (n = 193) from Canada, Brazil and South Africa; Group 2 (n = 206) from the HoFH International Clinical Collaboration. Pre- and post-treatment LDL-C and lipid lowering therapy (LLT) intensity from Group 1 were used to develop a regression model and applied to treated LDL-C in Group 2 to impute pre-treatment LDL-C. The same process was performed in reverse. A final imputation model was created from combining both groups. RESULTS: There was a curvilinear relationship between the expected and observed % lowering of LDL-C on LLT (r = 0.3923, p < 0.0001, Standard Error [SE] = 23 %). Using this relationship, LDL-C was imputed from treated values and showed significant correlation with pre-treatment LDL-C (r = 0.71, p < 0.001; mean values 13.4 ± 4.7 [Standard Deviation] and 13.6 ± 7.3 mmol/L, respectively, ns). Concordance between actual and imputed values ≥ 10 or <10 mmol/L was 80 %. Whereas 36 % of patients had treated LDL-C ≥ 10 mmol/L, 64 % had treated or imputed pre-treatment LDL-C ≥ 10 mmol/L. CONCLUSIONS: In HoFH, the response to LLT can be quantified and used to impute untreated LDL-C from treated LDL-C. Imputation may augment awareness of possible HoFH in treated subjects lacking records of untreated LDL-C.
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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 | 0.084 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".