MIBLood-EV: An Online Reporting Tool to Facilitate the Standardized Reporting of Preanalytical Variables and Quality Control of Plasma and Serum to Enhance Rigor and Reproducibility in Liquid Biopsy Research
Bibliographic record
Abstract
Pre-analytical variability significantly impacts the reproducibility of liquid biopsy research, which is critical for precision medicine and biomedical research. This report highlights the challenges and variability in the pre-analytical processes of liquid biopsies, especially regarding extracellular vesicles (EVs), which are crucial for diagnostics in oncology. The MIBlood-EV initiative aims to standardize the reporting of pre-analytical variables and the quality control of plasma and serum samples to enhance reproducibility in EV research. By providing a comprehensive and flexible reporting framework, MIBlood-EV seeks to improve the reliability of EV studies and facilitate the development of evidence-based protocols, ultimately advancing the field of liquid biopsy research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.086 | 0.464 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".