Identification of primary polydipsia in a severe and persistent mental illness outpatient population: A prospective observational study
Bibliographic record
Abstract
Studies to date have only investigated primary polydipsia in hospitalized psychiatric patient populations, where rates range from 3% to 25%. The objective of the present study was to determine the occurrence of primary polydipsia in a psychiatric outpatient population, and to determine the perceptions of outpatients with self-induced water intoxication regarding reasons for drinking excess fluids, health risks, and insight into their behavior. All 115 psychiatric outpatients from a Community Outreach Program in Kingston, Ontario, were invited to participate in this study. Of these, 89 (77.4%) were enrolled. Data collection included chart reviews, structured interviews, weight measurements, and urine collection. The incidence of primary polydipsia was found to be 15.7%. One-half of the polydipsic people presenting with medical complications suggestive for water intoxication had cigarette smoking as a strong correlate. There were interesting answers to the self-induced water intoxication questionnaire. These showed a lack of knowledge related to the normal quantity of fluids necessary daily and about healthy behaviors. Excessive drinking occurs in psychiatric patient populations outside of institutional/hospital settings. Patients have limited awareness of the severity and possible complications from their problem. Given the prevalence of polydipsia, more effort should be put into identifying and treating this problem.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".