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Record W3020087243 · doi:10.1016/j.jcf.2020.04.012

A multinational report to characterise SARS-CoV-2 infection in people with cystic fibrosis

2020· article· en· W3020087243 on OpenAlex

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

VenueJournal of Cystic Fibrosis · 2020
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsSt. Michael's HospitalCystic Fibrosis Canada
Fundersnot available
KeywordsCystic fibrosisMedicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakMultinational corporationVirologyPandemicBetacoronavirusPathologyInternal medicineInfectious disease (medical specialty)DiseaseOutbreak

Abstract

fetched live from OpenAlex

Information is lacking on the clinical impact of the novel coronavirus, SARS-CoV-2, on people with cystic fibrosis (CF). Our aim was to characterise SARS-CoV-2 infection in people with cystic fibrosis. METHODS: Anonymised data submitted by each participating country to their National CF Registry was reported using a standardised template, then collated and summarised. RESULTS: 40 cases have been reported across 8 countries. Of the 40 cases, 31 (78%) were symptomatic for SARS-CoV-2 at presentation, with 24 (60%) having a fever. 70% have recovered, 30% remain unresolved at time of reporting, and no deaths have been submitted. CONCLUSIONS: This early report shows good recovery from SARS-CoV-2 in this heterogeneous CF cohort. The disease course does not seem to differ from the general population, but the current numbers are too small to draw firm conclusions and people with CF should continue to strictly follow public health advice to protect themselves from 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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.020
GPT teacher head0.314
Teacher spread0.294 · 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