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Record W4283829776 · doi:10.1016/s2666-5247(22)00123-9

Effects of therapies for Ebola virus disease: a systematic review and network meta-analysis

2022· review· en· W4283829776 on OpenAlexaff
Ya Gao, Yunli Zhao, Gordon Guyatt, Robert Fowler, Richard Kojan, Long Ge, Jinhui Tian, Janet Dı́az, Marta Lado, Daniel Youkee, Aasim Ahmad, Cindy W. Albertson, Séverine Caluwaerts, Modet Camara, Ian Crozier, Hilde De Clerck, Susanna Dunachie, William A. Fischer, Bushra Jamil, Patrice Kabongo, Patricia Kabuni, Charline Kahambu Ngorombi, Maurice Kakule, Marie-Claire Kolié, Sulaiman Lakoh, Hans-Jörg Lang, Isekusu Mpinda Fiston, P. M. Mulumba, Srinivas Murthy, Sorie Samura, Rachel Couban, Qiukui Hao

Bibliographic record

VenueThe Lancet Microbe · 2022
Typereview
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsPublic Health OntarioHealth Sciences CentreSunnybrook Health Science CentreHamilton Health SciencesUniversity of TorontoMcMaster UniversityImpact
FundersNational Institute for Health and Care ResearchFrederick National Laboratory for Cancer ResearchChina Scholarship CouncilWorld Health Organization
KeywordsEbola virusMeta-analysisVirologyDiseaseMedicineVirusInternal medicine

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.031
metaresearch head score (Gemma)0.067
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.031
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.067
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0240.052
Bibliometrics0.0110.010
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.154
GPT teacher head0.410
Teacher spread0.256 · 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 designMeta-analysis
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

Citations23
Published2022
Admission routes1
Has abstractyes

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