Alterations of Hematological Parameters, Hemoglobin and Hematocrit With Liver Enzymes, Aspartate Transaminase and Alanine Transaminase Among Patients With Chronic Kidney Disease Undergoing Hemodialysis in Aljouf Region, Saudi Arabia
Bibliographic record
Abstract
BACKGROUND: Anemia results from low kidney production of the erythropoiesis-stimulant erythropoietin. Good liver function is crucial to patients with chronic kidney disease (CKD). This study analyzed two hematological parameters (hemoglobin (Hb) and hematocrit (Ht)) and two liver enzymes, alanine aminotransferase (ALT) and aspartate aminotransferase (AST) in patients with CKD undergoing hemodialysis (HD). METHODS: Three hundred and thirty individuals participated in this study. Of them, 159 patients with CKD undergoing HD and 171 healthy individuals as a control group were recruited between January and October 2017. Blood was collected into EDTA containers for complete blood count (CBC) and into additive plain containers for ALT and AST. Relevant case history data were also taken. RESULTS: Obtained results for first and second month of Hb, Ht, AST and ALT of CKD patients undergoing HD were lower (10.2 g/dL and 8.9 for Hb; 27.2% and 24.8% for Ht; 17.8 IU/L and 15.6 IU/L for ALT; 13.9 IU/L and 12.0 IU/L for AST, respectively) (P < 0.05) compared to healthy individuals (15.1 g/dL for Hb; 44.6% for Ht; 28.3 IU/L for ALT and 23.0 IU/L for AST). Furthermore, Hb, Ht, AST and ALT levels were significantly lower (P < 0.05) in their second month compared to the first month. CONCLUSIONS: Anemia might emerge in CKD patients because of low Hb concentration and consequent low Ht. Advanced stage CKD causes decreased AST and ALT, as a result of lipid metabolism disturbances. Therefore, anemia and liver diseases are recommended to be treated in CKD patients to alleviate related complications. Renal transplantation must be performed earlier for them to avoid further complications.
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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 | 0.000 |
| 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".