Evaluation of Serum Electrolyte Levels in Patients With Anemia
Bibliographic record
Abstract
INTRODUCTION: Anemia is one of the most prevalent diseases globally. Various diseases have linked anemia with electrolyte disturbance. However, the local data are limited. In this study, we will determine the prevalence of electrolyte imbalance in anemic patients. METHODS: This case-control study was conducted in a tertiary care hospital from January 2021 to July 2021. A total of 500 anemic patients were enrolled in the study after informed consent. Another 500 non-anemic patients were enrolled as the control group. Blood was taken from both groups and send for assessment of electrolytes. RESULTS: Sodium levels were significantly lower in anemic patients compared to non-anemic patients (131.42 ± 0.82 meq/L vs. 135.57 ± 0.42 meq/L; p-value: <0.0001). Potassium levels were significantly higher in anemic patients compared to non-anemic participants (4.37 ± 0.12 meq/L vs. 4.09 ± 0.11 meq/L; p-value: <0.0001). Chloride levels were significantly higher in participants with anemia compared to non-anemic participants (103.92 ± 0.46 meq/L vs. 100.99 ± 0.41 meq/L). CONCLUSION: Our study indicates that sodium levels and potassium levels are impacted in patients with anemia compared to patients without anemia. Close monitoring of serum electrolytes is suggested in patients with anemia to avoid complications and life-threatening conditions.
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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 | 0.001 |
| 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 | 0.001 | 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".