Secondary Nephrogenic Diabetes Insipidus as a Complication of Inherited Renal Diseases
Bibliographic record
Abstract
BACKGROUND/AIMS: Nephrogenic diabetes insipidus (NDI) is a serious condition with large water losses in the urine and the risk of hypernatremic dehydration. Unrecognized, repeated episodes of hypernatremic dehydration can lead to permanent brain damage. Primary NDI is due to mutations in either AVPR2 or AQP2. NDI can also occur as a secondary complication, most commonly from obstructive uropathy or chronic lithium therapy. We observed NDI in patients with inherited tubulopathies and aimed to define the clinical and molecular phenotype. METHODS: We reviewed the medical notes of 4 patients with clinical NDI and an underlying molecularly confirmed diagnosis of nephropathic cystinosis, Bartter syndrome, nephronophthisis and apparent mineralocorticoid excess, respectively. RESULTS: The patients all failed to concentrate their urine after administration of 1-desamino[8-D-arginine] vasopressin. None had an identifiable mutation in AVPR2 or AQP2, consistent with secondary NDI. Patients experienced repeated episodes of hypernatremic dehydration, and in 2 cases, NDI was initially thought to be the primary diagnosis, delaying recognition of the underlying problem. CONCLUSION: The recognition of this potential complication is important as it has direct implications for clinical management. The occurrence of NDI in association with these conditions provides clues for the etiology of aquaporin deficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".