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Record W4301606283 · doi:10.7554/elife.80556.sa2

Author response: An international observational study to assess the impact of the Omicron variant emergence on the clinical epidemiology of COVID-19 in hospitalised patients

2022· peer-review· en· W4301606283 on OpenAlexaff
Bronner P. Gonçalves, Matthew Hall, Waasila Jassat, Valeria Balan, Srinivas Murthy, Christiana Kartsonaki, Malcolm G. Semple, Amanda Rojek, Joaquín Baruch, Luis Felipe Reyes, Abhishek Dasgupta, Jake Dunning, Barbara Wanjiru Citarella, Mark G. Pritchard, Alejandro Martín‐Quirós, Uluhan Sili, J. Kenneth Baillie, Diptesh Aryal, Yaseen M. Arabi, Aasiyah Rashan, Andrea Angheben, Janice Caoili, François Martin Carrier, Ewen M. Harrison, Joan Gómez‐Junyent, Claudia Figueiredo‐Mello, James Joshua Douglas, Mohd Basri Mat Nor, Yock Ping Chow, Xin Ci Wong, Silvia Bertagnolio, Soe Soe Thwin, Leonardo Salazar, Asgar Rishu, Rajavardhan Rangappa, David S. Y. Ong, Madiha Hashmi, Gail Carson, Janet Dı́az, Rob Fowler, Moritz U. G. Kraemer, Evert‐Jan Wils, Peter Horby, Laura Merson, Piero Olliaro

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreLions Gate HospitalUniversité de MontréalCentre Hospitalier de l’Université de MontréalUniversity of British Columbia
Fundersnot available
KeywordsObservational studyEpidemiologyCoronavirus disease 2019 (COVID-19)MedicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Internal medicineDisease

Abstract

fetched live from OpenAlex

Combined analyses of publicly available population-level variant data and detailed individual-level clinical data can be used to quantify the clinical impact of new SARS-CoV-2 variants in different settings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.019
metaresearch head score (Gemma)0.185
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: Observational
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.019
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.185
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.002

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 head0.548
GPT teacher head0.599
Teacher spread0.051 · 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
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

Citations0
Published2022
Admission routes1
Has abstractyes

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