Use of clozapine to treat psychogenic polydipsia in schizoaffective disorder—A case report
Bibliographic record
Abstract
Psychogenic polydipsia (PPD) is a well-recognized condition characterized by excessive volitional water intake and often seen in patients with chronic mental illness, particularly schizophrenia and schizoaffective disorders. Literature is inconclusive around the treatment of PPD. This is case report of a 68-year old woman with past history of schizoaffective disorder – bipolar type, who was admitted to a specialized psychiatric facility for treatment. At the time of admission, she scored 10 on Positive Symptoms Scale (PSS) indicating severe psychosis. During her inpatient stay she was found to consume more than two liters of fluid daily which was soon followed by confusion, agitation, and gait imbalance. At that time, her serum sodium was 113 mmol/L which indicated significant hyponatremia. Through assessment and consultation with an endocrinology specialist, non-psychogenic polydipsia was ruled out and the patient was treated first with Haloperidol, Acetazolamide and finally Clozapine which was found to stabilize her psychotic symptoms as well as PPD. At the time of discharge, patient's serum sodium level was 143 mmol/L and her PSS score was 3. In this case, clozapine was effective in treating psychosis as well as PPD. Given that this patient was an older adult, this report indicates the feasibility of clozapine treatment, if tolerated well, to the geriatric population.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".