Electrolyte Analysis and Replacement: Challenging a Paradigm in Surgical Patients
Bibliographic record
Abstract
Postoperative patients are susceptible to alterations in electrolyte homeostasis. Although electrolytes are replaced in critically ill patients, stable asymptomatic non-intensive care unit (ICU) patients often receive treatment of abnormal electrolytes. We hypothesize there is no proven benefit in asymptomatic patients. In 2016, using the electronic medical records and pharmacy database at a university academic medical center, we conducted a retrospective cost analysis of the frequency and cost of electrolyte analysis (basic metabolic panel [BMP], ionized calcium [Ca], magnesium [Mg], and phosphorus [P]) and replacement (potassium chloride [KCl], Mg, oral/iv Ca, oral/iv P) in perioperative patients. Patients without an oral diet order, with creatinine more than 1.4, age less than 16 years, admitted to the ICU, or with length of stay of more than 1 week were excluded. Nursing costs were calculated as a fraction of hourly wages per laboratory order or electrolyte replacement. One hundred thirteen patients met our criteria over 11 months. Mean length of stay was 4 days; mean age was 54 years; and creatinine was 0.67 ± 0.3. Electrolyte analysis laboratory orders (n = 1,045) totaled $6,978, and BMP was most frequently ordered accounting for 36% of laboratory costs. In total, 683 doses of electrolytes cost the pharmacy $1,780. Magnesium was most frequently replaced, followed by KCl, P, and Ca. Nursing cost associated with electrolyte analysis/replacement was $7,782. There is little evidence to support electrolyte analysis and replacement in stable asymptomatic noncritically ill patients, but their prevalence and cost ($146/case) in this study were substantial. Basic metabolic panels, pharmacy charges for potassium, and nursing staff costs accounted for the most significant portion of the total cost. Considering these data, further research should determine whether these practices are warranted.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".