Considerations for extrapolating<i>in vivo</i>bioequivalence data across species and routes
Bibliographic record
Abstract
Modric, S., Bermingham, E., Heit, M., Lainesse, C., Thompson, C. Considerations for extrapolatingin vivobioequivalence data across species and routes.J. vet. Pharmacol. Therap.35(Suppl. 1), 45–52. The purpose of this article is to discuss the numerous species‐specific and route‐specific factors that can influence the peak and extent of exposure of an active pharmaceutical ingredient as they relate to the demonstration of bioequivalence between veterinary drug products (test and reference formulations). Evaluation of potential circumstances when species‐to‐species or route‐to‐route extrapolations of bioequivalence data could be considered is provided, together with suggestions for alternative statistical analysis. It is concluded that further research is much needed in this area to establish an appropriate scientific basis for across‐species and across‐route comparisons.
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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 | 0.284 | 0.409 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| 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".