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Record W3211795092 · doi:10.1038/s41591-021-01552-x

Reporting guidelines for human microbiome research: the STORMS checklist

2021· review· en· W3211795092 on OpenAlexaff
Chloe Mirzayi, Audrey Renson, Cesare Furlanello, Susanna‐Assunta Sansone, Fatima Zohra, Shaimaa Elsafoury, Ludwig Geistlinger, Lora J. Kasselman, Kelly Eckenrode, Janneke van de Wijgert, Amy Loughman, Francine Z. Marques, David A. MacIntyre, Manimozhiyan Arumugam, Rimsha Azhar, Francesco Beghini, Kirk Bergstrom, Ami S. Bhatt, Jordan E. Bisanz, Jonathan Braun, Héctor Corrada Bravo, Gregory A. Buck, Frederic D. Bushman, David Casero, Gerard Clarke, María Carmen Collado, Paul D. Cotter, John F. Cryan, Ryan T. Demmer, Suzanne Devkota, Eran Elinav, Juan S. Escobar, Jennifer M. Fettweis, ROBERT FINN, Anthony A. Fodor, Sofia K. Forslund, André Franke, Jack Gilbert, Elizabeth A. Grice, Benjamin Haibe‐Kains, Scott A. Handley, Pamela Herd, Susan Holmes, Jonathan P. Jacobs, Lisa Karstens, Rob Knight, Dan Knights, Omry Koren, Douglas S. Kwon, Morgan G. I. Langille, Brianna Lindsay, Dermot McGovern, Alice C. McHardy, Shannon K. McWeeney, Noel T. Mueller, Luigi Nezi, Matthew R. Olm, Noah W. Palm, Edoardo Pasolli, Jeroen Raes, Matthew R. Redinbo, Malte Rühlemann, R. Balfour Sartor, Patrick D. Schloss, Lynn M. Schriml, Eran Segal, Michelle Shardell, Thomas J. Sharpton, Ekaterina Smirnova, Harry Sokol, Justin L. Sonnenburg, Sujatha Srinivasan, Louise B. Thingholm, Peter J. Turnbaugh, Vaibhav Upadhyay, Ramona Walls, Paul Wilmes, Takuji Yamada, Georg Zeller, Mingyu Zhang, Ni Zhao, Liping Zhao, Wenjun Bao, Aedín C. Culhane, Viswanath Devanarayan, Joaquı́n Dopazo, Xiaohui Fan, Matthias Fischer, Wendell Jones, Rebecca Kusko, Christopher E. Mason, Tim R. Mercer, Andreas Scherer, Leming Shi, Shraddha Thakkar, Weida Tong, Russ Wolfinger, Chris Hunter, Nicola Segata, Curtis Huttenhower, Jennifer B. Dowd, Heidi E. Jones, Levi Waldron

Bibliographic record

VenueNature Medicine · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsDalhousie UniversityPrincess Margaret Cancer CentreUniversity Health NetworkUniversity of British Columbia, Okanagan Campus
FundersAccelerated Innovation Research Initiative Turning Top Science and Ideas into High-Impact ValuesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of General Medical SciencesBiotechnology and Biological Sciences Research CouncilNational Cancer InstituteU.S. Department of Health and Human ServicesNational Institutes of HealthMinistry of Education, Culture, Sports, Science and TechnologyNational Institute of Environmental Health SciencesLeverhulme TrustNovo Nordisk Fonden
KeywordsChecklistMicrobiomeMedicineData sciencePsychologyBiologyBioinformaticsComputer science

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 distilled prediction

Teacher imitation

Not 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)0.006
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.193
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.409
GPT teacher head0.595
Teacher spread0.185 · 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 teacher head, not a consensus.

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

Citations471
Published2021
Admission routes1
Has abstractno

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