MétaCan
Menu
Back to cohort
Record W3039971219 · doi:10.1016/j.ajpath.2020.07.001

Association of Initial Viral Load in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Patients with Outcome and Symptoms

2020· article· en· W3039971219 on OpenAlexfundno aff
Kimon V. Argyropoulos, António Serrano, Jiyuan Hu, Margaret A. Black, Xiaojun Feng, Guomiao Shen, Melissa Call, Min Jae Kim, Andrew Lytle, Brendan Belovarac, Theodore Vougiouklakis, Lawrence Hsu Lin, Una Moran, Adriana Heguy, Andrea B. Troxel, Matija Snuderl, Iman Osman, Paolo Cotzia, George Jour

Post-publication record

NatureRetraction
ReasonDate of Article and/or Notice Unknown;Duplication of Content through Error by Journal/Publisher;Removed;
Date7/7/2020 0:00
Flagged by OpenAlex?Yes

Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.

Bibliographic record

VenueAmerican Journal Of Pathology · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsnot available
FundersNational Cancer InstituteNational Institutes of HealthYork University
KeywordsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)Betacoronavirus2019-20 coronavirus outbreakMedicineCoronavirusViral loadSars virusRespiratory systemVirologySevere acute respiratory syndrome coronavirusSeverity of illnessImmunologyVirusPathologyOutbreakInternal medicineDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
metaresearch head score (Gemma)0.003
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.045
GPT teacher head0.417
Teacher spread0.372 · 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

Citations204
Published2020
Admission routes1
Has abstractno

Explore more

Same venueAmerican Journal Of PathologySame topicCOVID-19 Clinical Research StudiesFrench-language works237,207