Bibliographic record
Abstract
As next-generation whole exome sequencing (WES) and whole genome sequencing (WGS) become increasingly available and affordable, their application has led to the growing identification of monogenic forms of rheumatic diseases, traditionally recognized as complex diseases. This is demonstrated in Batu, et al ’s paper, “Whole Exome Sequencing in Early-onset Systemic Lupus Erythematosus,” appearing in this issue of The Journal 1. The authors completed WES on 7 Turkish patients with systemic lupus erythematosus (SLE), with disease onset at 5 years of age or younger, from multiplex families (proband had an affected sibling) or who were offspring of consanguineous parents. They found 5 patients who were homozygous for variants predicted to alter the early complement cascade proteins. The sixth patient was homozygous for a 2-base pair deletion in DNASE1L3 , a variant previously associated with young-onset SLE and hypocomplementemic urticarial vasculitis2,3,4. The seventh patient was homozygous for a number of variants, with an HDAC7 variant deemed a potentially causal variant. The Batu, et al paper1 highlights how the identification of causal genetic variants leading to monogenic lupus not only provides insights into the probable pathogenic variants responsible for disease and rare forms of monogenic lupus, but that these variants also implicate pathogenic mechanisms in SLE more broadly. One such example is DNASE1L3, an enzyme responsible for clearance of genetic material from apoptotic cellular debris. … Address correspondence to Dr. L.T. Hiraki. E-mail: linda.hiraki{at}sickkids.ca
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".