Predictive factors of low HCO3 levels in peritoneal dialysis patients
Bibliographic record
Abstract
BACKGROUND: Metabolic acidosis is a major metabolic abnormality in end-stage renal disease (ESRD) and alkali is provided with dialysis treatment to patients on chronic peritoneal dialysis (CPD) to keep their acid-base balance within normal serum HCO3- levels. METHODS AND RESULTS: We examined the levels of venous serum HCO3- in 163 patients on CPD and the predictive factors for HCO3- levels low enough to indicate metabolic acidosis. The mean value for HCO3- was 26+/-2.4 mmol/l and for anion gap was 13.1+/-3.1 mEq/l. A serum bicarbonate concentration of less than 24 mmol/l, compatible with metabolic acidosis, was observed in 13.5% of the patients. In a multivariate analysis HCO3- levels were directly correlated with older age and use of CaCO3- as phosphate binders, and inversely associated with serum potassium, the use of sevelamer and low lactate dialysis solutions. Higher serum urea levels, the use of low lactate solutions and sevelamer instead of CaCO3 were significantly predictive factors for HCO3- levels < 24 mmol/l. CONCLUSIONS: Venous HCO3- and anion gap values were within the normal ranges in stable CPD patients. In 13.5% of them, however, chronic metabolic acidosis was observed based on venous HCO3- levels < 24 mmol/l. Dietary protein intake, the use of sevelamer and low (35 mmol/l) concentration of lactate in dialysis solutions are important predictive factors for chronic metabolic acidosis in these patients.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".