From Prediction Tools to Precision Medicine in Antineutrophil Cytoplasmic Antibody–Associated Vasculitis
Bibliographic record
Abstract
Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitides (AAVs) are multiorgan autoimmune disorders resulting in irreversible organ damage. Left untreated, AAVs are fatal, and aggressive immunosuppressive treatment has transformed them into chronic relapsing conditions. Outcomes in ANCA-associated glomerulonephritis (ANCA-GN) remain unsatisfactory, and kidney failure is too common for this rare condition. The presentation of ANCA kidney disease is heterogenous, yet a one-size-fits-all approach is still used for its treatment due to a missing disease stratification. ANCA-GN confers significant morbidity and mortality. The severity of kidney disease has demonstrated its significance with the different stages of kidney function at presentation established as independent risk factors for mortality. Kidney failure, measured as an estimated glomerular filtration rate (eGFR) of less than 15 mL/min/1.73 m2 at the time of diagnosis, confers a significantly higher risk of mortality.1 Accurate and reliable prediction tools are needed both to inform patients and their families of prognosis and to be able to stratify patients for more efficient interventional trials in the future. Given the rarity of the condition, recruiting to these trials can be difficult and stratification by prognosis can reduce the required sample size.2 Aggressive immunosuppression is the first-line treatment; however, it may not suit all patients, particularly those who are frail. A reliable risk stratification will allow treatment to be personalized and yield better outcomes. This approach is already well established in oncology—where there are rigorous criteria for disease staging and patient fitness—and has seen vast improvements in survival over the past decades. In this … Address correspondence Dr. S.R. Brix, Renal, Transplantation and Urology Unit, Manchester University NHS Foundation Trust, Manchester Royal Infirmary, Oxford Road, Manchester, M13 9WL, UK. Email: silke.brix{at}manchester.ac.uk.
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 | 0.016 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| 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".