Using hypernatraemic events to predict reduced renal function in elderly lithium patients: a brief report
Bibliographic record
Abstract
BACKGROUND: Diabetes insipidus (DI) is a recognized adverse effect of lithium use, and studies have shown an association between decreased renal function and DI in patients using lithium. We hypothesize that hypernatraemic events that occur in DI predict decreased renal function in elderly patients on lithium. METHODS: We conducted a retrospective cohort study involving 55 geriatric psychiatry patients using lithium between 1985 and 2010. Patients who always had sodium levels ≤146 mmol/L were compared to patients with one or more episodes of hypernatraemia (serum sodium level ≥147 mmol/L) for estimated glomerular filtration rate (eGFR) levels and prevalence of severe chronic renal failure (eGFR ≤30 mL/min/1.73 m(2)). RESULTS: eGFR was found to be less in the hypernatraemic group than in the non-hypernatraemic controls (41 vs 56 mL/min/1.73 m(2); P = 0.0074). Severe chronic renal failure appeared more prevalent in hypernatraemic patients (4/14 (28.6%) vs. 3/41 (7.3%)), but this did not achieve statistical significance (P = 0.061). The two groups did not differ for age, sex, medical comorbidities or other clinical variables, except antidepressant use. Hypernatraemic patients appeared less likely to use antidepressants than non-hypernatraemic patients, odds ratio = 0.69 (P = 0.020). However, in multivariate analysis, hypernatraemia correlated with decreased eGFR (β = -0.39, P = 0.004), while antidepressant use did not (P = 0.81). CONCLUSIONS: These results suggest that hypernatraemic events may predict reduced renal function in geriatric patients using lithium. The role of hypernatraemia and DI in renal failure in this population requires further study. Health professionals should be aware of the risks of renal failure in older patients treated with lithium, especially in the context of sodium level abnormalities.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".