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Record W4398583041 · doi:10.7910/dvn/gi8yl9

Replication Data for: Systemic inflammation and metabolic disturbances underlie inpatient mortality among ill children with severe malnutrition

2021· dataset· en· W4398583041 on OpenAlexaff
Bijun Wen, James M. Njunge, Céline Bourdon, Gerard Bryan Gonzales, Bonface M. Gichuki, Dorothy Lee, Moses M. Ngari, Emmanuel Chimwezi, Johstone Thitiri, Laura Mwalekwa, Wieger Voskuijl, James A. Berkley, Robert Bandsma

Bibliographic record

VenueHarvard Dataverse · 2021
Typedataset
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsSystemic inflammationMalnutritionReplication (statistics)InflammationMedicineIntensive care medicineInternal medicineVirology

Abstract

fetched live from OpenAlex

This is a case-control study nested within the F75 intervention trial. The trial is registered at clinicaltrials.gov (NCT02246296). By comparing the metabolomic profiles between cases (children who died, n=90) and controls (children who were discharged alive, n=90), this study aimed to understand the metabolic pathways underlying mortality among children with severe malnutrition admitted to hospitals in Kenya and Malawi. Targeted serum metabolomics were performed using liquid chromatography and tandem mass spectrometry (LC-MS/MS). A total 206 metabolites were targeted by two assays - the AbsoluteIDQ p180 Kit quantifying 188 metabolites and the TMIC PRIME® assay quantifying 18 organic acids.

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)0.004
metaresearch head score (Gemma)0.025
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.070
Threshold uncertainty score0.233

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0700.018

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 head0.047
GPT teacher head0.313
Teacher spread0.266 · 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
GenreDataset

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

Citations1
Published2021
Admission routes1
Has abstractyes

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