Monitoring and reporting the composition of plasma and serum to improve biobanks and comparability of extracellular vesicle research: communication from the ISTH SSC Subcommittee on Vascular Biology
Bibliographic record
Abstract
Transparent reporting is key to improving the reproducibility of scientific research. In 2023, the International Society for Extracellular Vesicles updated the "Minimal information for studies of extracellular vesicles" (MISEV) reporting guidelines and published new recommendations for blood extracellular vesicle (EV) research entitled "MIBlood-EV: Minimal information to enhance the quality and reproducibility of blood extracellular vesicle research." The MIBlood-EV recommendations are part of MISEV 2023 and promote reporting not only the protocols used for blood collection and handling but also the composition of the prepared samples that are used to measure EVs. Plasma and serum are commonly used starting materials for EV research; reporting their composition can help to improve reproducibility, comparison of measurement results, and support evidence-based guideline development. We conducted an online survey among the International Society on Thrombosis and Haemostasis (ISTH) EV researchers. Of the 20 respondents, 95% were familiar with MISEV, but 35% were unaware of the 2023 update, and only 65% applied these guidelines to their reports. With regard to MIBlood-EV, 40% were unaware of this reporting tool, and 20% did not follow its recommendations. This is surprising because most respondents agree that preanalytical variables of blood EV research are not satisfactorily described (75%), confirm that having a standardized reporting tool is beneficial for blood EV research (90%), and consider MIBlood-EV applicable to other fields of ISTH research (80%). In this Scientific and Standardization Committee communication, we summarize the survey results, as well as the background and goals of MISEV and how MIBlood-EV can be useful to improve the reproducibility of blood research within the ISTH community.
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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 | 0.308 | 0.432 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier 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".