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Record W3095493910 · doi:10.1111/nep.13814

Acute kidney injury associated with <scp>COVID</scp>‐19—Cumulative evidence and rationale supporting against direct kidney injury (infection)

2020· article· en· W3095493910 on OpenAlex
Malvinder S. Parmar

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNephrology · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsNOSM University
Fundersnot available
KeywordsTropismMedicineAcute kidney injuryTMPRSS2KidneyImmunologyVirusSerine proteaseProteasePathogenesisSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)VirologyCoronavirus disease 2019 (COVID-19)PathologyInternal medicineBiologyEnzyme

Abstract

fetched live from OpenAlex

Acute kidney injury (AKI) is a common complication, affecting up to 37% of hospitalized patients with SARS-CoV-2 infection and is proportional to its severity and portends poor prognosis. Diverse mechanisms have been proposed and studies reported conflicting results. Moreover, renal tropism of SARS-CoV-2 does not equate to its renal pathogenicity. For a virus to be pathogenic, in addition to its affinity (tropism) for specific tissue(s), host cells must allow viral entry, and discuss the important role played by transmembrane protease, serine 2 (TMPRSS2) and coexpression of both ACE2 and TMPRSS2 in the same cells is important to cause damage. Lack of coexpression of ACE2 and TMPRSS2 in the same cells of the kidneys is the limiting factor of SARS-CoV-2 direct effects in the kidney. We present the rationale and cumulative evidence supporting that AKI is secondary to hemodynamic and immunologic effects of SARS-CoV-2 infection than the direct injury or infection.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.268
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.276
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.268
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.057
GPT teacher head0.397
Teacher spread0.339 · 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