MétaCan
Menu
Back to cohort
Record W3094321790 · doi:10.1056/nejmc2030446

RAAS Inhibitors and Risk of Covid-19

2020· letter· en· W3094321790 on OpenAlexaff

Bibliographic record

VenueNew England Journal of Medicine · 2020
Typeletter
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsNorth York General Hospital
Fundersnot available
KeywordsRenal functionMedicineCreatinineCoronavirus disease 2019 (COVID-19)DiseasePopulationImpaired renal functionInternal medicineIntensive care medicineUrologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

tent with a drug-induced process 3 but rather with the patients' underlying disease.Treatment with givosiran was associated with small increases in creatinine (0.07 mg per deciliter at 3 months) in the overall trial population, changes that were mainly reversible.Some patients with preexisting renal disease had reductions in renal function that stabilized with ongoing administration of givosiran.However, consistent with labeling, monitoring of renal function during givosiran treatment is recommended as clinically indicated.

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)0.014
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.011
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0110.007
Insufficient payload (model declined to judge)0.0080.004

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 head0.059
GPT teacher head0.403
Teacher spread0.344 · 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
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

Citations23
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueNew England Journal of MedicineSame topicCOVID-19 Clinical Research StudiesFrench-language works237,207