Management of Diabetes Insipidus following Surgery for Pituitary and Suprasellar Tumors
Bibliographic record
Abstract
Central diabetes insipidus (CDI) is a common complication after pituitary surgery. However, it is most frequently transient. It is defined by the excretion of an abnormally large volume of dilute urine with increasing serum osmolality. The reported incidence of CDI after pituitary surgery ranges from 0-90%. Large tumour size, gross total resection and intraoperative cerebrospinal fluid leak usually pose an increased risk of CDI as observed with craniopharyngioma and Rathke's cleft cysts. CDI can be associated with high morbidity and mortality if not promptly recognised and treated on time. It is also essential to rule out other causes of postoperative polyuria to avoid unnecessary pharmacotherapy and iatrogenic hyponatremia. Once the diagnosis of CDI is established, close monitoring is required to evaluate the response to treatment and to determine whether the CDI is transient or permanent. This review outlines the evaluation and management of patients with CDI following pituitary and suprasellar tumour surgery to help recognise the diagnosis, consider the differential diagnosis, initiate therapeutic interventions and guide monitoring and long-term management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".