Discrepancies in pharmacokinetic analysis results obtained by using two standard population pharmacokinetics software programs
Bibliographic record
Abstract
Multiple standard software packages for population pharmacokinetics (PK) modeling are currently available. These programs may significantly vary in the algorithms used for modeling plasma concentrations as a function of time course. We compared the population PK parameters obtained by using two standard software packages, p-pharm and saam ii, for analysis of a similar data set of serum samples of doxorubicin obtained from 11 infants and children with malignant diseases. Plasma drug concentrations were fitted to time by a two-compartment intra-vascular PK model by saam ii and p-pharm programs. The population parameters obtained from the analysis by the two software programs were substantially different. For example, Vd was almost five times larger when using saam ii compared with p-pharm (9.6 L/kg vs. 2.0 L/kg, respectively), whereas t((1/2)beta) was about 30 times larger in the latter (7.7 h vs. 206.9 h, respectively). When considering the results reported from a population PK analysis, validation of the results by different software should be considered, especially when extreme, unexpected values are obtained.
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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 | 0.026 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".