Blood Lithium Monitoring Practices in a Population-Based Sample of Older Adults
Bibliographic record
Abstract
OBJECTIVE: Lithium is an effective treatment for mood disorders, but lithium level and renal monitoring every 3 months is recommended in older patients treated with lithium to prevent serious adverse events. This study examined lithium monitoring practices in a large geriatric cohort. METHODS: This population-based cohort study (N = 11,006) used linked health care administrative databases. Older lithium users (n = 5,503; mean age = 70.6 years) in Ontario, Canada, enrolled between April 1, 2002, and March 31, 2014, were propensity score matched 1:1 to valproate users (n = 5,503). The frequency with which serum lithium levels were monitored and renal and endocrine laboratory testing was done during a 1-year follow-up period was examined. RESULTS: The baseline characteristics of the 2 groups were similar. At least 1 serum lithium concentration recorded within 90, 180, and 365 days of follow-up was present in 24.1%, 42.4%, and 66.8% of lithium users, respectively. Corresponding numbers for serum creatinine were 29.6%, 50.4%, and 75.4%, respectively. While serum creatinine monitoring (hazard ratio [HR] = 1.19; 95% CI, 1.12-1.27; P < .001), thyroid-stimulating hormone monitoring (HR = 1.47; 95% CI, 1.37-1.58; P < .001), and calcium testing (HR = 1.15; 95% CI, 1.02-1.29; P = .018) were statistically higher in lithium compared to valproate users, absolute differences between groups were not clinically meaningful. CONCLUSIONS: In a geriatric Canadian community sample, lithium monitoring was infrequent and inconsistent with international standards that call for screening of lithium levels and renal function every 3 months.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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".