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Record W3194257905 · doi:10.1038/s41592-021-01206-3

The power of imaging to understand extracellular vesicle biology in vivo

2021· review· en· W3194257905 on OpenAlexaff
Frederik J. Verweij, Leonora Balaj, Chantal M. Boulanger, David R. F. Carter, Ewoud B. Compeer, Gisela D’Angelo, Samir EL Andaloussi, Jacky G. Goetz, Julia Christina Gross, Vincent Hyenne, Eva‐Maria Krämer‐Albers, Charles Pin‐Kuang Lai, Xavier Loyer, Alex Márki, Stefan Momma, Esther N. M. Nolte-‘t Hoen, D. Michiel Pegtel, Héctor Peinado, Graça Raposo, Kirsi Rilla, Hidetoshi Tahara, Clotilde Théry, Martin E. van Royen, Roosmarijn E. Vandenbroucke, Ann M. Wehman, Kenneth W. Witwer, Zhiwei Wu, Richard Wubbolts, Guillaume van Niel

Bibliographic record

VenueNature Methods · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsCanadian Nautical Research Society
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of Mental HealthBiotechnology and Biological Sciences Research CouncilInstitut National Du CancerInstitut National de la Santé et de la Recherche MédicaleUniversité de StrasbourgBiocenter FinlandLigue Contre le CancerNational Institute on Drug AbuseAgence Nationale de la RechercheNational Cancer InstituteCancer Research UK
KeywordsExtracellular vesiclesBiodistributionVesicleExtracellular vesicleBiologyCell biologyNanotechnologyLive cell imagingComputational biologyBiophysicsIn vivoMicrovesiclesCellBiochemistryMaterials scienceBiotechnologyMembrane

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0000.002
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.024
GPT teacher head0.422
Teacher spread0.398 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations334
Published2021
Admission routes1
Has abstractno

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