<p>The Impact of Intravenous Fluid Therapy on Acid-Base Status of Critically Ill Adults: A Stewart Approach-Based Perspective</p>
Bibliographic record
Abstract
One of the most important tasks of physicians working in intensive care units (ICUs) is to arrange intravenous fluid therapy. The primary indications of the need for intravenous fluid therapy in ICUs are in cases of resuscitation, maintenance, or replacement, but we also load intravenous fluid for purposes such as fluid creep (including drug dilution and keeping venous lines patent) as well as nutrition. However, in doing so, some facts are ignored or overlooked, resulting in an acid-base disturbance. Regardless of the type and content of the fluid entering the body through an intravenous route, it may impair the acid-base balance depending on the rate, volume, and duration of the administration. The mechanism involved in acid-base disturbances induced by intravenous fluid therapy is easier to understand with the help of the physical-chemical approach proposed by Canadian physiologist, Peter Stewart. It is possible to establish a quantitative link between fluid therapy and acid-base disturbance using the Stewart principles. However, it is not possible to accomplish this with the traditional approach; moreover, it may not be noticed sometimes due to the normalization of pH or standard base excess induced by compensatory mechanisms. The clinical significance of fluid-induced acid-base disturbances has not been completely clarified yet. Nevertheless, as fluid therapy may be the cause of unexplained acid-base disorders that may lead to confusion and elicit unnecessary investigation, more attention must be paid to understand this issue. Therefore, the aim of this paper is to address the effects of different types of fluid therapies on acid-base balance using the simplified perspective of Stewart principles. Overall, the paper intends to help recognize fluid-induced acid-base disturbance through bedside evaluation and choose an appropriate fluid by considering the acid-base status of a patient.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".