MO032CKD REMISSION IN A PROSPECTIVE COHORT OF PEOPLE WITH CKD STAGE 3 RECRUITED FROM PRIMARY CARE
Bibliographic record
Abstract
Introduction and Aims: International guidelines provide criteria for the diagnosis of chronic kidney disease (CKD) and to identify CKD progression. There is, however, no consensus on when CKD should be considered no longer present and there is therefore a lack of data on how frequently this occurs. Here we report on CKD remission in a population previously meeting criteria for CKD. Additionally we show that this outcome (like adverse outcomes) can be predicted using simple clinical variables. Methods: 1741 participants were individually and prospectively recruited from local general practice surgeries. All had CKD stage 3 prior to recruitment, defined by two eGFR readings of 30-59 ml/min/1.73m2. At baseline, year 1 and year 5 study visits, demographic data and medical history was collected and blood samples collected for biochemistry. Participants also submitted 3 early-morning urine samples from consecutive days. For CKD remission, we required both an eGFR >60 ml/min/1.73m2 and uACR <3mg/mmol (average of 3 values) at the year 5 visit. We used the KDIGO definition of CKD progression, requiring a 25 % decline in eGFR and an increase in eGFR category, or an increase in albuminuria category. Mortality data were collected from national records (office of national statistics).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".