High-Anion-Gap Metabolic Acidosis During a Prolonged Hospitalization Following Perforated Diverticulitis: An Educational Case Report
Bibliographic record
Abstract
Rationale: The metabolic acidoses are generally separated into 2 categories on the basis of an anion gap calculation: high-anion-gap and normal anion-gap metabolic acidosis. When a high-anion-gap metabolic acidosis (HAGMA) is not clearly explained by common etiologies and routine confirmatory testing, specialized testing can definitively establish rare diagnoses such as 5-oxoproline, d-lactate accumulation, or diethylene glycol toxicity. Presenting Concerns of the Patient: A 56-year-old woman had a prolonged hospital admission following perforated diverticulitis requiring sigmoid resection. Her hospitalization was complicated by feculent peritonitis and surgical wound dehiscence needing prolonged broad-spectrum antibiotics and wound debridements. She developed acute kidney injury and HAGMA in the hospital. Diagnoses: Chart review showed that she received a large cumulative dose of acetaminophen during her hospital stay. Laboratory studies showed markedly increased serum 5-oxoproline causing HAGMA. Interventions Including Prevention and Lifestyle: -acetylcysteine and renal replacement therapy. Outcomes: After admission to the intensive care unit, the patient continued to require vasopressor and ventilatory support for septic shock and a ventilator-associated pneumonia. After an initial recovery and resolution of her HAGMA, she subsequently suffered recurrent aspirations which were fatal. Teaching points: 1. The acronym GOLD MARK is useful when assessing patients with HAGMA and most causes of HAGMA can be established with routine testing.2. When the etiology of HAGMA remains unclear, additional testing can be required to diagnose rare causes of HAGMA.3. Rare causes of HAGMA are diethylene glycol, 5-oxoproline, and d-lactate accumulation.4. Acidosis secondary to 5-oxoproline accumulation can occur even with "therapeutic" doses of acetaminophen in patients receiving it regularly for a prolonged period and who have depleted glutathione stores.5. Risk factors for glutathione depletion include malnutrition, older age, sepsis, pregnancy, multiple chronic illnesses, and chronic kidney disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".