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The GA4GH Phenopacket schema defines a computable representation of clinical data
Julius O.B. Jacobsen, Michael Baudis, Gareth Baynam, J. Beckmann, Sergi Beltrán, Orion J. Buske, Tiffany J. Callahan, Christopher G. Chute, Mélanie Courtot, Daniel Daniš, Olivier Elemento, Andrea Essenwanger, Robert R. Freimuth, Michael Gargano, Tudor Groza, Ada Hamosh, Nomi L. Harris, Rajaram Kaliyaperumal, K. C. Kent Lloyd, Aly Khalifa, Peter Krawitz, Sebastian Köhler, Bryan Laraway, Heikki Lehväslaiho, Leslie Matalonga, Julie A. McMurry, Alejandro Metke‐Jimenez, Chris Mungall, Mónica Muñoz-Torres, Soichi Ogishima, Anastasios Papakonstantinou, Davide Piscia, Nikolas Pontikos, Núria Queralt-Rosiñach, Marco Roos, Julian Saß, Paul N. Schofield, Dominik Seelow, Anastasios Siapos, Damian Smedley, Lindsay Smith, Robin Steinhaus, Jagadish Chandrabose Sundaramurthi, Emilia M. Swietlik, Sylvia Thun, Nicole Vasilevsky, Alex H. Wagner, Jeremy L. Warner, Claus Weiland, Myles Axton, Lawrence Babb, Cornelius F. Boerkoel, Bimal P. Chaudhari, Hui‐Lin Chin, Michel Dumontier, Nour Gazzaz, Harry Hochheiser, Veronica A. Kinsler, Hanns Lochmüller, Alexander Mankovich, Gary Saunders, Panagiotis I. Sergouniotis, Rachel Thompson, Andreas Zankl, Melissa Haendel, Peter N. Robinson
Bibliographic record
VenueNature Biotechnology · 2022
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsOttawa HospitalChildren's Hospital of Eastern OntarioUniversity of OttawaUniversity of British ColumbiaOntario GenomicsB.C. Women's Hospital & Health CentreOntario Institute for Cancer Research
FundersLawrence Berkeley National LaboratoryUniversity of California Davis School of MedicineUniversity College LondonUniversité de LausanneNational Cancer InstituteNational Human Genome Research InstituteLeibniz-GemeinschaftCanadian Institutes of Health ResearchAnschutz Medical Campus, University of ColoradoNational Institutes of HealthGenome CanadaEuropean Molecular Biology LaboratoryAngela Wright Bennett FoundationNational Health and Medical Research CouncilVanderbilt UniversityMoorfields Eye CharityMoorfields Eye Hospital NHS Foundation TrustCommonwealth Scientific and Industrial Research OrganisationFaculty of Health and Medical Sciences, University of Western AustraliaEuropean Bioinformatics InstituteFreie Universität BerlinDepartment of Artificial Intelligence, Korea UniversityUniversität ZürichU.S. Department of Health and Human ServicesEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentHumboldt-Universität zu BerlinAustralian e-Health Research CentreAlan Turing InstituteNational Center for Advancing Translational SciencesMedical Research CouncilLeids Universitair Medisch CentrumUniversiteit LeidenUniversity of ConnecticutWeill Cornell Medical CollegeQueen Mary University of LondonNational Institute for Health and Care ResearchStan Perron Charitable FoundationUniversitat Pompeu FabraWellcome TrustUniversitat de BarcelonaOhio State UniversityBarcelona Institute of Science and TechnologyChina Scholarship CouncilJapan Agency for Medical Research and DevelopmentJohns Hopkins UniversityMcCusker Charitable FoundationBerlin Institute of HealthNationwide Children's HospitalU.S. Department of Energy
KeywordsSchema (genetic algorithms)Representation (politics)Computer scienceInformation retrievalPolitical science
Abstract
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 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.
metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.234
Threshold uncertainty score1.000
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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.
Opus teacher head0.039
GPT teacher head0.355
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation status
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.
Study designNot applicable
Domainnot available
GenreCommentary
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
Citations111
Published2022
Admission routes2
Has abstractno