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Record W4295747815 · doi:10.1016/s0140-6736(22)01585-9

The Lancet Commission on lessons for the future from the COVID-19 pandemic

2022· review· en· W4295747815 on OpenAlexaff
Jeffrey D. Sachs, Salim S. Abdool Karim, Lara B. Aknin, Joseph R. Allen, Kirsten Brosbøl, Francesca Colombo, Gabriela Cuevas Barron, María Fernanda Espinosa, Ví­tor Gaspar, Alejandro Gaviría, Andy Haines, Peter J. Hotez, Phoebe Koundouri, Felipe Larraín Bascuñán, Jong‐Koo Lee, Muhammad Ali Pate, Gabriela Ramos, K. Srinath Reddy, Ismail Serageldin, John Thwaites, Vaira Vīķe-Freiberga, Chen Wang, Miriam Were, Lan Xue, Chandrika Bahadur, María Elena Bottazzi, Chris Bullen, George Laryea-Adjei, Yanis Ben Amor, Guillaume Lafortune, Emma Torres, Lauren Barredo, Juliana G E Bartels, Neena Joshi, Margaret Hellard, Uyen Kim Huynh, Shweta Khandelwal, Jeffrey V. Lazarus, Susan Michie

Bibliographic record

VenueThe Lancet · 2022
Typereview
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)CommissionVirologyBetacoronavirusCoronavirus InfectionsMedicinePolitical scienceInfectious disease (medical specialty)LawDiseasePathologyOutbreak

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)0.009
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.003
Science and technology studies0.0010.003
Scholarly communication0.0060.006
Open science0.0020.005
Research integrity0.0110.012
Insufficient payload (model declined to judge)0.0200.006

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 head0.499
GPT teacher head0.533
Teacher spread0.033 · 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 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

Citations869
Published2022
Admission routes1
Has abstractno

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