Severe hyponatraemia in medical in-patients: aetiology, assessment and outcome
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Hyponatraemia is the most commonly identified electrolyte abnormality. Published data on severe hyponatraemia in general medical in-patients is lacking. AIM: To determine the aetiology, adequacy of assessment, and outcome of severe hyponatraemia in general medical in-patients. DESIGN: Retrospective case-note review. METHODS: All general medical in-patients (n = 108) with serum sodium < or =125 mmol/l were identified from the clinical chemistry database, over a six-month period. A full review of notes and computer records was undertaken at the index date and a pre-determined follow-up date. RESULTS: Follow-up data were available in 105 patients. There was a wide range of aetiologies: diuretic therapy (loop and thiazide), congestive cardiac failure and liver disease were the most common, and 75.3% of patients had multiple causes. None of the 48% of patients whose history suggested a possible diagnosis of the syndrome of inappropriate anti-diuretic hormone (SIADH) met the generally accepted diagnostic criteria. Overall mortality was 20% during the index admission and 44.6% at follow-up, vs. 7.1% and 22%, respectively, for other patients admitted to the same directorate over the same time period (p < 0.001). Mortality was linked to aetiology, but not to reduced absolute serum sodium concentration at admission. DISCUSSION: Severe hyponatraemia in general medical patients is associated with a complex, multifactoral aetiology and a very poor prognosis. Outlook is governed principally by aetiology, and not by serum sodium level. Assessment of patients with hyponatraemia requires a practical clinical algorithm for diagnosing SIADH.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it