MétaCan
Menu
Back to cohort
Record W3157263169 · doi:10.1038/s41586-021-03570-8

COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets

2021· article· en· W3157263169 on OpenAlexfundno aff
Toni Delorey, Carly G.K. Ziegler, Graham Heimberg, Rachelly Normand, Yiming Yang, Åsa Segerstolpe, Domenic Abbondanza, Stephen J. Fleming, Ayshwarya Subramanian, Daniel T. Montoro, Karthik A. Jagadeesh, Kushal K. Dey, Pritha Sen, Michal Slyper, Yered Pita-Juárez, Devan Phillips, Jana Biermann, Zohar Bloom‐Ackermann, Nikolaos Barkas, Andrea Ganna, James Gomez, Johannes C. Melms, Igor Katsyv, Erica Normandin, Pourya Naderi Yeganeh, Yury Popov, Siddharth S. Raju, Sebastian Niezen, Linus Tsai, Katherine J. Siddle, Malika Sud, Victoria M. Tran, Shamsudheen Karuthedath Vellarikkal, Yiping Wang, Liat Amir-Zilberstein, Deepak Atri, Joseph Beechem, Olga R. Brook, Jonathan Chen, Prajan Divakar, Phylicia Dorceus, J Engreitz, Adam L. Essene, Donna M. Fitzgerald, Robin Fropf, Steven Gazal, Joshua Gould, John Grzyb, Tyler Harvey, Jonathan L. Hecht, Tyler Hether, Judit Jané‐Valbuena, Michael Leney-Greene, Hui Ma, Cristin McCabe, Daniel E. McLoughlin, Eric Miller, Christoph Muus, Mari Niemi, Robert F. Padera, Liuliu Pan, Deepti Pant, Carmel Pe’er, Jenna Pfiffner-Borges, Christopher J. Pinto, Jacob Plaisted, Jason Reeves, Marty Ross, Melissa A. Rudy, Erroll H. Rueckert, Michelle Siciliano, Alexander Sturm, Ellen Todres, Avinash Waghray, Sarah Warren, Shuting Zhang, Daniel R. Zollinger, Lisa A. Cosimi, Rajat M. Gupta, Nir Hacohen, Hanina Hibshoosh, Alkes L. Price, Jayaraj Rajagopal, Purushothama Rao Tata, Stefan Riedel, Gyöngyi Szabó, Timothy L. Tickle, Patrick T. Ellinor, Deborah T. Hung, Pardis C. Sabeti, Richard Novák, Robert Rogers, Donald E. Ingber, Z. Gordon Jiang, Dejan Juric, Mehrtash Babadi, Samouil L. Farhi, Benjamin Izar, James R. Stone, Ioannis S. Vlachos, Isaac H. Solomon, Orr Ashenberg, Caroline Porter, Bo Li, Alex K. Shalek, Alexandra–Chloé Villani, Orit Rozenblatt–Rosen, Aviv Regev

Bibliographic record

VenueNature · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Cancer InstituteNational Institute on Alcohol Abuse and AlcoholismIrving Medical Center, Columbia UniversityDefense Advanced Research Projects AgencyNational Institutes of HealthHamilton Health Sciences FoundationManton FoundationHoward Hughes Medical InstituteChan Zuckerberg InitiativeNational Human Genome Research InstituteWellcome TrustNational Institute of Mental HealthNational Heart, Lung, and Blood InstituteBeth Israel Deaconess Medical CenterBurroughs Wellcome FundNational Institute of General Medical SciencesMassachusetts General HospitalDamon Runyon Cancer Research Foundation
KeywordsLungBiologyStromal cellPathologyImmune systemAutopsyCell typeKidneyDiffuse alveolar damageCellImmunologyMedicineAcute respiratory distressGenetics

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.277
Teacher spread0.262 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations878
Published2021
Admission routes1
Has abstractno

Explore more

Same venueNatureSame topicSingle-cell and spatial transcriptomicsFrench-language works237,207