Quality of laboratory biomarker monitoring during treatment with lithium in patients with bipolar disorder
Bibliographic record
Abstract
BACKGROUND: Clinical guidelines recommend monitoring of creatinine and lithium throughout treatment with lithium. We here assessed the extent to which this occurs in healthcare in Sweden. METHODS: This is an observational study of all adults with bipolar disorder starting lithium therapy in Stockholm, Sweden, during 2007-2018. The main outcome was monitoring of blood lithium and creatinine at therapy initiation and/or once annually. The secondary outcome was monitoring of calcium and thyroid-stimulating hormone (TSH). Patients were followed up until therapy cessation, death, out-migration, or to the end of 2018. RESULTS: We identified 4428 adults with bipolar disorder who started lithium therapy and were followed up for up to 11 years. Their median age was 39 years, and 63% were women. The median duration on lithium therapy was 4.3 (IQR: 1.9-7.45) years, and the majority who discontinued therapy started another mood stabilizer soon after. Overall, 21% started lithium therapy without assessing the serum/plasma concentration of creatinine. The proportion of people who did not have both lithium and creatinine measured increased from 21% in the first year to 33% in the eleventh year. The proportion with annual testing for TSH or calcium was slightly lower. As few as 16% of patients had both lithium and creatinine tested once annually during their complete time on lithium. CONCLUSIONS: In a Swedish community sample, lithium and creatinine monitoring was inconsistent with guideline recommendations that call for measurement of annual biomarker levels.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".