MIBlood‐EV: Minimal information to enhance the quality and reproducibility of blood extracellular vesicle research
Bibliographic record
Abstract
Blood is the most commonly used body fluid for extracellular vesicle (EV) research. The composition of a blood sample and its derivatives (i.e., plasma and serum) are not only donor-dependent but also influenced by collection and preparation protocols. Since there are hundreds of pre-analytical protocols and over forty variables, the development of standard operating procedures for EV research is very challenging. To improve the reproducibility of blood EV research, the International Society for Extracellular Vesicles (ISEV) Blood EV Task Force proposes standardized reporting of (i) the applied blood collection and preparation protocol and (ii) the quality of the prepared plasma and serum samples. Gathering detailed information will provide insight into the performance of the protocols and more effectively identify potential confounders in the prepared plasma and serum samples. To collect this information, the ISEV Blood EV Task Force created the Minimal Information for Blood EV research (MIBlood-EV), a tool to record and report information about pre-analytical protocols used for plasma and serum preparation as well as assays used to assess the quality of these preparations. This tool does not require modifications of established local pre-analytical protocols and can be easily implemented to enhance existing databases thereby enabling evidence-based optimization of pre-analytical protocols through meta-analysis. Taken together, insight into the quality of prepared plasma and serum samples will (i) improve the quality of biobanks for EV research, (ii) guide the exchange of plasma and serum samples between biobanks and laboratories, (iii) facilitate inter-laboratory comparative EV studies, and (iv) improve the peer review process.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reporting · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Reporting · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.165 | 0.417 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.020 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".