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Record W2986543078 · doi:10.7554/elife.15651.014

Author response: Reproducible diagnostic metabolites in plasma from typhoid fever patients in Asia and Africa

2017· peer-review· en· W2986543078 on OpenAlexaff
Elin Näsström, Christopher M. Parry, Nga Tran Vu Thieu, Rapeephan R. Maude, Hanna K de Jong, Masako Fukushima, Olena Rzhepishevska, Florian Marks, Ursula Panzner, Justin Im, Hyonjin Jeon, Seeun Park, Zabeen Chaudhury, Aniruddha Ghose, Rasheda Samad, Tan Trinh Van, Anders Johansson, Arjen M. Dondorp, Guy Thwaites, Abul Faiz, Henrik Antti, Stephen Baker

Bibliographic record

Venuenot available
Typepeer-review
Languageen
FieldNursing
TopicVitamin C and Antioxidants Research
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsTyphoid feverSalmonella typhiBlood cultureMedicineMalariaInternal medicineImmunologyVirologyBiologyMicrobiologyAntibiotics

Abstract

fetched live from OpenAlex

Mass spectrometry on plasma from patients with typhoid fever and other febrile disease identified and validated 24 metabolites that can distinguish typhoid from other febrile diseases, providing a new approach for typhoid diagnostics.

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)0.007
metaresearch head score (Gemma)0.057
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.993
Threshold uncertainty score0.193

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.057
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0580.024

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 head0.061
GPT teacher head0.364
Teacher spread0.303 · 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.

Study designNot applicable
DomainReproducibility
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
Published2017
Admission routes1
Has abstractyes

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