Hypercalcemia and Upper GI Symptoms
Bibliographic record
Abstract
Hypercalcemia is a common clinical presentation encountered in day to day practice. The most common causes of hypercalcemia have been shown to be primary hyperparathyroidism and hypercalcemia of malignancy accounting for around 90% of all cases. We present the case of a 78 year old male admitted to hospital with delirium and an uncommon cause of hypercalcemia, milk alkali syndrome (MAS) in the context of Peptic ulcer disease (PUD). MAS, caused by excessive intake of calcium based antacids, was first identified in the 1920’s when the Sippy regimen, consisting of milk or cream and calcium salts was used to treat peptic ulcers. With the advent of proton pump inhibitors and histamine-2 blockers, it had become a rare cause of hypercalcemia responsible for <1% of the cases. However, there has been a re-emergence of MAS due to the increased use of calcium supplements for osteoporosis prevention and over-the-counter preparations for dyspepsia. Interestingly, apart from the well-known classic symptoms of hypercalcemia, chronic hypercalcemia also increases gastric acid production and worsens PUD which in this case, could likely have lead to increased calcium—based antacid ingestion, worsening the hypercalcemia and creating a vicious cycle. This case report explores the physiology of the connection between hypercalcemia and PUD and emphasizes the need to keep MAS on the differential diagnosis for hypercalcemia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".