Challenges of Therapeutic Drug Monitoring and Usage of Lithium in Managing Psychiatric Disorders: A Review
Bibliographic record
Abstract
Lithium is referred by all key guidelines as a first-line treatment for bipolar disorder (BD) maintenance therapy. Therapeutic drug monitoring (TDM) is necessary while using lithium as lithium treatment. The concern arose when there is one article discussed about the declining of usage of lithium in certain countries such as US and Canada. However, the prescription rates of lithium in United Kingdom and Scandinavia do not show a downward trend. The aim of this study was to determine the challenges of TDM and usage of lithium in managing psychiatric disorders. A total of 32 articles have mentioned the challenges in TDM and usage of lithium. Descriptive statistics was done to identify the challenges in TDM and usage of lithium in psychiatric disorders. There is a total of 25 and 27 challenges found in TDM and usage of lithium in psychiatric disorders respectively. The most stated challenges in TDM of lithium were drug-drug and drug-disease interactions (16.07%), poor compliance (14.28%) and different therapeutic levels or dose suggested by different authors or guidelines (14.28%). The most stated challenges in usage of lithium in psychiatric disorders were potential toxicity (16.85%), side effects of lithium (16.85%), narrow therapeutic range (15.73%) and needs frequent monitoring (15.73%). However, the usage of lithium in certain countries remains still high may be due to the benefits of lithium to psychiatric patients in reducing risk of manic relapse and depressive relapse. Although there are 52 challenges found in the TDM and usage of lithium, there is no definite article or statement stating that the usage of lithium is declining. Future multi-national study is needed to study the extend challenges in TDM and usage of lithium in developed countries as compared to developing countries.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".