<i>Et tu, brute force?</i> An approach to refine blood sample collection protocols
Bibliographic record
Abstract
Reducing the number of samples collected on individual animals during an experiment has various advantages; however, it is critical that any refinements to a sampling protocol retain a similar degree of accuracy. The objective of this study was to develop a refined blood sampling protocol capable of reproducing the original findings of a pilot study using only a subset of the collected samples. Data from a previous study were available consisting of plasma concentrations of Cr and Co collected at 15 timepoints over a 64 h period that were used to calculate area under the curve (AUC). Prior to subsequent analysis, it was determined that an adequate reduced sampling protocol must estimate the original AUC within 10%. A SAS program was prepared that generated all possible combinations of sample timepoints. Optimal sets of timepoints were identified by minimal root mean square prediction error. Output from this procedure can be evaluated for bioequivalence using the two one-sided tests approach. This method highlights the ability to further refine biological sampling protocols, which can be readily applied to other experimental settings.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".