Acidosis in a patient with cholera: a need to redefine concepts
Bibliographic record
Abstract
A patient presented with cholera and a severe degree of ECF volume contraction. Despite large losses of bicarbonate (HCO3-)-containing diarrhoeal fluid, laboratory acid-base values were remarkably close to normal. A detailed analysis emphasizing principles of physiology and a quantitative approach provided new insights and eventually better definitions of metabolic and respiratory acidosis. A shift in focus from HCO3- concentration to HCO3- content in the extracellular fluid (ECF) compartment revealed the presence of metabolic acidosis. Central to this analysis was an emphasis on the haematocrit to enable a more accurate estimate of the degree of ECF volume contraction. The latter also revealed 'contraction' metabolic alkalosis, which masked the underlying metabolic acidosis. The presence of a respiratory acidosis of the tissue type was evident from the raised venous PCO2, which was not surprising once the magnitude of the ECF contraction had been appreciated. 'Bad buffering', as defined by Professor McCance, was the immediate danger and prompted swift action to restore an effective circulation. The haematocrit and the venous PCO2 also contribute valuable information to monitor the response to therapy. Nevertheless, there were still dangers to be discovered when an in-depth analysis suggested that the administration of isotonic saline would introduce an unanticipated danger for the 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".