Bibliographic record
Abstract
In this issue of The Journal , co–first authors Kim, Bang, and colleagues present provocative data suggesting an association with the clinical response to cyclophosphamide (CYC) therapy in patients with active lupus nephritis: whether administered according to the US National Institutes of Health (NIH) regimen of monthly intravenous doses or according to the Euro-Lupus biweekly schedule, response to CYC is associated with single-nucleotide polymorphism (SNP) in the telomeric end of the FCGR gene cluster adjacent to FCGR2B on chromosome 1q231. The FCGR2B gene encodes the cell surface receptor, FcγRIIb (CD32B), which is the only Fcγ receptor with a tyrosine-based inhibitory motif in the human genome. The prospect of genetically based personalized, precision medicine coming to rheumatologic conditions apart from certain autoinflammatory and rare disease states2 and specific considerations in pharmacogenomics3 is very appealing and holds high promise for the future. The reasonable question is whether we have arrived or have more to do. The study by Kim and Bang’s group defines complete, partial, and non-responsiveness to CYC and relates these clinical categories to nearly 500,000 SNP in germline DNA obtained in a genome-wide association study. The technology is powerful and continues to advance with more densely featured SNP genotyping arrays and even with whole-genome sequencing, which is now both technically and financially within reach. Thus, the … Address correspondence to Dr. R.P. Kimberly; E-mail: rpk{at}uab.edu
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".