Uncovering the basis of a severe degree of acidemia in a patient with diabetic ketoacidosis
Bibliographic record
Abstract
In this teaching exercise, the goal is to demonstrate how an application of principles of physiology can reveal the basis for a severe degree of acidaemia (pH 6.81, bicarbonate <3 mmol/l (P(HCO(3))), PCO(2) 8 mmHg), why it was tolerated for a long period of time, and the issues for its therapy in an 8-year-old female with diabetic ketoacidosis. The relatively low value for the anion gap in plasma (19 mEq/l) suggested that its cause was both a direct and an indirect loss of NaHCO(3). Professor McCance suggested that ileus due to hypokalaemia might cause this direct loss of NaHCO(3), and that an excessive excretion of ketoacid anions without NH(4)(+) in the urine accounted for the indirect loss of NaHCO(3). In addition, he suspected that another factor also contributing to the severity of the acidaemia was a low input of alkali. He was also able to explain why there was a 16-h delay before there was a rise in the P(HCO(3)) once therapy began. The missing links in this interesting story, including a possible basis for the hypokalaemia, emerge during the discussion between the medical team and Professor McCance.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".