The Biochemical Profile of Familial Hypocalciuric Hypercalcemia and Primary Hyperparathyroidism during Pregnancy and Lactation: Two Case Reports and Review of the Literature
Bibliographic record
Abstract
Background. Primary hyperparathyroidism (PHPT) and Familial Hypocalciuric Hypercalcemia (FHH) result in different maternal and fetal complications in pregnancy. Calcium to creatinine clearance ratio (CCCR) is commonly used to help distinguish these two conditions. Physiological changes in calcium handling during pregnancy and lactation can alter CCCR, making it a less useful tool to distinguish PHPT from FHH. Cases. A 25-year-old female presented with hypercalcemia and an inappropriately normal PTH. Her CCCR was 0.79% before pregnancy and rose to 1.99% in her second trimester. The proband’s mother and neonate had asymptomatic hypercalcemia. Genetic analysis revealed a CaSR mutation consistent with FHH. A 19-year-old female presented with a history of nephrolithiasis who underwent emergent caesarean section at 29 weeks of gestation for severe preeclampsia. At delivery, she was diagnosed with hypercalcemia with an inappropriately normal PTH and a CCCR of 2.67%, which fell to 0.88% during lactation. Parathyroidectomy cured her hypercalcemia. Pathology confirmed a parathyroid adenoma. Conclusion. These cases illustrate the influence of pregnancy and lactation on renal calcium indices, such as the CCCR. To avoid diagnostic error of women with hypercalcemia during pregnancy and lactation, calcium biochemistry of first-degree relatives and genetic testing of select patients are recommended.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".