Clinical characteristics and outcomes of new uremic patients with extreme azotemia in southern Taiwan
Bibliographic record
Abstract
Serum creatinine (SCr) had been considered to be an important predictor of mortality in end-stage renal disease (ESRD) patients at the start of renal replacement therapy (RRT). However, the data were limited about initially extreme azotemia (EA), exclusively defined as blood urea nitrogen (BUN) > or = 300 mg/dL, SCr > or = 30 mg/dL, or both. This retrospective study was conducted to clarify the characteristics and outcome in our EA patients. We had 1682 new ESRD patients from July 1988 to December 1996. With frequency match for age, gender, and starting RRT in the same period, 20 EA patients and 60 controls were included. Fifty percent of our EA patients had unknown etiology. The EA patients had significantly lower prevalence of underlying diabetic nephropathy, and comorbid hypertension. All the EA patients had late referral to nephrologists within 4 weeks before the initiation of RRT, and 90% of them had taken Chinese herbals. The EA group had significantly higher BUN, SCr, and iron storage as well as a higher prevalence of severe anemia, hyperkalemia, hypocalcemia, and acidemia. However, the similar prevalence of cardiomegaly and left ventricular hypertrophy as well as the similar early mortality rate and long-term survival were noted. Age over 40 years, comorbid diabetes mellitus, and hypoalbuminemia were independent predictors of poor survival. Our EA patients had different initial presentations from other uremic ones at the start of RRT. However, the short-term and long-term mortality rates were similar. The lower prevalence of underlying diabetic nephropathy and comorbid hypertension among the EA patients might contribute to their fair outcome.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".