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Record W3080462543 · doi:10.1016/j.jinf.2020.08.021

Serum cholinesterase associated with COVID-19 pneumonia severity and mortality

2020· letter· en· W3080462543 on OpenAlexaboutno aff
Kento Nakajima, Takeru Abe, Ryo Saji, Fumihiro Ogawa, Hayato Taniguchi, Keishi Yamaguchi, Kazuya Sakai, Tomoki Nakagawa, Reo Matsumura, Yasufumi Oi, Mototsugu Nishii, Ichiro Takeuchi

Bibliographic record

VenueJournal of Infection · 2020
Typeletter
Languageen
FieldMedicine
TopicCholinesterase and Neurodegenerative Diseases
Canadian institutionsnot available
FundersUniversité de Bourgogne
KeywordsMedicineInternal medicineGastroenterologySepsisPneumoniaCholinesteraseButyrylcholinesteraseAcetylcholinesteraseAchéEnzymeBiology

Abstract

fetched live from OpenAlex

Kunutsor and Laukkanen have written to this journal regarding elevated admission levels of markers of liver injury (alanine aminotransferase and aspartate aminotransferase, gamma-glutamyltransferase, alkaline phosphatase and total bilirubin) may be associated with progression to severe disease or death in COVID-19.1 On the other hand, serum cholinesterase plays an important role in the inflammatory response and may be associated with prognosis in sepsis.2–4 We focused on the similarities between severe COVID-19 pneumonia and sepsis.

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)0.002
metaresearch head score (Gemma)0.009
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: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.051
GPT teacher head0.328
Teacher spread0.277 · 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
GenreEmpirical

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

Citations32
Published2020
Admission routes1
Has abstractyes

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