Is environmental temperature related to renal symptoms, serum lithium levels, and other laboratory test results in current lithium users?
Bibliographic record
Abstract
OBJECTIVE: Lithium continues to be an important mood disorder treatment. Although patients exposed to higher environmental temperatures may have serum lithium level elevations due to dehydration, there is conflicting data in the literature. In addition, no study has assessed the association between temperature and other renal laboratory tests and symptoms in lithium users. METHODS: This is a cross-sectional analysis of 63 current lithium users who participated in the McGill Geriatric Lithium-induced Diabetes Insipidus Clinical Study. The relationship between mean daily temperature with diabetes insipidus symptoms, glomerular filtration rate, urine osmolality, serum sodium, lithium level, and lithium dose-level ratio was assessed. RESULTS: Although a higher temperature on the day of laboratory testing trended toward being independently associated with a lower lithium dose-level ratio (Beta = -0.17, p = 0.08), this was not found when using a dichotomous measure of temperature (T > 20°C). No association was observed between temperature and other renal parameters. CONCLUSIONS: The association of temperature with lithium levels, renal symptoms, and laboratory tests appears to be of relatively little clinical importance in lithium users in temperate climates. However, future research should re-examine patients living in climates with extreme temperatures (e.g., >40°C), who may theoretically be at higher risk.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".