A comparison of five approaches for lithium dose and serum concentration prediction
Bibliographic record
Abstract
Various methodologies are proposed in the literature to predict lithium dose or serum concentration. The authors compare the performance of a fuzzy system modeling algorithm that has been proposed with four other algorithms in terms of their performance based on serum lithium concentration prediction. The first method is the "Zetin method" proposed by M. Zetin et al. (1986), which is based on stepwise multiple linear regressions and designed specifically for lithium pharmacokinetics. Secondly, the more recent method proposed by T. Terao et al. (1999). The third method is a new formula developed from the data on hand by using stepwise multiple linear regressions. Fourth, a comparison is made with the well-known fuzzy system modeling algorithm proposed by M. Sugeno and T.A. Yasukawa (1993). The proposed method is the fifth alternative to be considered in this comparison. Published data from 30 patients (T. Terao et al. 1999) were used in the analysis. The performance of the algorithms with respect to precision as measured by the root mean square error are as follows: 0.54, 0.34, 0.36, 0.31 and 0.24 mmol/L, respectively for the Zetin method, Terao method, stepwise multiple linear regression, Sugeno-Yasukawa approach and the new algorithm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".