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Record W4394759140 · doi:10.1016/s1473-3099(24)00155-5

Increased faecal shedding in SARS-CoV-2 variants BA.2.86 and JN.1

2024· letter· en· W4394759140 on OpenAlexaff
Dhammika Leshan Wannigama, Mohan Amarasiri, Phatthranit Phattharapornjaroen, Cameron Hurst, Charin Modchang, Sudarat Chadsuthi, Suparinthon Anupong, Kazuhiko Miyanaga, Longzhu Cui, Stefan Fernandez, Angkana T. Huang, Puey Ounjai, Andrew C. Singer, Naveen Kumar Devanga Ragupathi, Daisuke Sano, Takashi Furukawa, Kazunari Sei, Asada Leelahavanichkul, Talerngsak Kanjanabuch, Tanittha Chatsuwan, Paul G. Higgins, Asuka Nanbo, Anthony Kicic, Richard Siow, Sam Trowsdale, Parichart Hongsing, Aisha Khatib, Kenji Shibuya, Shuichi Abe, Hitoshi Ishikawa, Wanwara Thuptiang, Ali Hosseini Rad S.M., Porames Vatanaprasan, Dylan John Jay, Thammakorn Saethang, Sirirat Luk-in, Robin James Storer, Phitsanuruk Kanthawee, Ratana Tacharoenmuang

Bibliographic record

VenueThe Lancet Infectious Diseases · 2024
Typeletter
Languageen
FieldMedicine
TopicCOVID-19 diagnosis using AI
Canadian institutionsUniversity of Toronto
FundersFaculty of Science, Mahidol UniversitySahlgrenska AkademinFaculty of Medicine, Chulalongkorn UniversityKitasato UniversityMahidol UniversityJichi Medical UniversityChulalongkorn UniversityCharles Darwin UniversityFaculty of Health and Medical Sciences, University of Western AustraliaUniversitätsklinikum KölnNational Research CentreDeutsches Zentrum für InfektionsforschungUniversität zu KölnNaresuan UniversityKing Chulalongkorn Memorial Hospital
KeywordsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Viral sheddingBiologyFecesVirologyCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakGeneticsMicrobiologyMedicineVirusOutbreakInternal medicine

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)0.001
metaresearch head score (Gemma)0.009
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: none
Teacher disagreement score0.016
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0160.009
Insufficient payload (model declined to judge)0.0050.003

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 head0.036
GPT teacher head0.330
Teacher spread0.294 · 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

Citations36
Published2024
Admission routes1
Has abstractno

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