Bibliographic record
Abstract
Renal involvement in systemic lupus erythematosus (SLE) occurs in 40–50% of adult patients, is associated with increased morbidity and mortality, and results in endstage renal disease (ESRD) about 10% of patients – rates that have changed little over the last 2 decades despite modifications in therapeutic strategies1. In all of us there is a gradual decline in kidney function that occurs with age, with estimates of losses of 0.4–1 ml/min/year in those over 30 years of age being reported2,3. Assuming a normal glomerular filtration rate (GFR) of 100 ml/min in early adulthood and assuming no changes in this rate due to intercurrent illnesses, our kidney function is sufficient to last a lifetime. However, in the presence of renal disease, for example, diabetic nephropathy or polycystic kidney disease, rates of loss of kidney function can be 10-fold greater or more, in turn meaning there is a significant chance of needing renal replacement therapy in the form of dialysis or transplantation during a patient’s lifetime. In addition, modest reductions in GFR are associated with significant increases in overall and specific cardiovascular mortality. We know that certain interventions can slow down this rate, blood pressure control being the main factor, and specific interventions such as good glycemic control and novel sodium-glucose linked transporter (SGLT)2 inhibitors for diabetes, or vasopressin receptor antagonists for polycystic kidney disease, respectively. What about other conditions where kidney decline is not a continuous process but has a more episodic character, such as occurs in relapsing lupus nephritis (LN)? The aim of treatment in LN is to halt … Address correspondence to A.D. Salama, Royal Free Hospital, UCL Centre for Nephrology, Rowland Hill Street, London NW3 2PF, UK. E-mail: a.salama{at}ucl.ac.uk, alan.salama{at}nhs
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".