Bibliographic record
Abstract
Introduction and Aims: Chronic kidney disease (CKD) is a risk-factor for both cardiovascular (CV) and non-CV death.It is not known whether the risk is different for persons with stable vs. progressively declining glomerular filtration rate (GFR).Standardized mortality ratio (SMR) is the ratio between observed mortality in CKD and mortality in the general population.Our aim was to investigate the effect of progressive CKD on cause-specific SMR in a population-based cohort of CKD stage 3 (CKD3) patients in a well-defined European population.Methods: All persons with CKD3 according to the K/DOQI definition (GFR 30-59 ml/min/1.73m 2 ) in Tromsø, Norway, were identified from a complete database of all 248,560 measurements of serum creatinine made in the municipality between 1994 and 2003.GFR was estimated with the 4-variable MDRD study equation, and change in GFR ( GFR) with a multilevel linear regression model.Persons with GFR < 0 were classified as having progressive disease.Time and cause of death were ascertained on 311204 from the Norwegian Cause of Death Registry.Cause-specific mortality rates in the general population were obtained from Statistics Norway.SMR for the CKD3 cohort was estimated for death from CV disease, cancer and other causes separately for persons with and without progression and in three subgroups of age for each gender.Log(SMR) was analyzed in an inverse-variance weighted multiple linear regression analysis with age-group, gender, progression status and cause of death as independent variables.The regression coefficients were exponentiated to give a multiplicative model.Results: In the CKD3 cohort (n=1225), 10 year all-cause cumulative mortality (95% CI) was 0.74 (0.69 -0.79) for progressive and 0.60 (0.51 -0.70) for non-progressive disease.SMR (95% CI) for CV death was 2.14 (1.90 -2.42) for progressive and 1.36 (1.08 -1.71) for non-progressive disease.For cancer death, the corresponding SMRs were 1.50 (1.18 -1.90) and 2.00 (1.48 -2.71).In the regression analysis (see table), progression was associated with higher SMR, but for death from cancer, SMR was higher for non-progressive disease.There were no other statistically significant interactions between the independent variables (p>0.05).Conclusions: Progressive CKD3 was associated with a higher mortality than non-progressive, except for cancer.A high SMR for CV death indicates that patients with progression should be targets of more intensive efforts to prevent and treat CV disease.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.176 | 0.059 |
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".