Practical Approach to Physical-Chemical Acid-Base Management. Stewart at the Bedside
Bibliographic record
Abstract
The late Peter Stewart developed an approach to the analysis of acid-base disturbances in biological systems based on basic physical-chemical principles. His key argument was that the traditional carbon dioxide/bicarbonate analysis with just the use of the Henderson-Hasselbalch equation does not account for the important role in the regulation of H(+) concentration played by strong ions, weak acids and water itself. Acceptance of his analysis has been limited because it requires a complicated set of calculations to account for all the variables and it does not provide simple clinical guidance. However, the analysis can be made more pragmatic by using a series of simple equations to quantify the major processes in acid-base disturbances. These include the traditional PCO2 component and the addition of four metabolic processes, which we classify as "water-effects," "chloride-effects," "albumin effects," and "others." Six values are required for the analysis: [Na(+)], [Cl(-)], pH, Pco2, albumin concentration, and base excess. The advantage of this approach is that it gives a better understanding of the mechanisms behind acid-base abnormalities and more readily leads to clinical actions that can prevent or correct the abnormalities. We have developed a simple free mobile app that can be used to input the necessary values to use this approach at the bedside (Physical/Chemical Acid Base Calculator).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.016 |
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".