Severe hyponatraemia in medical in-patients: aetiology, assessment and outcome
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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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".