MétaCan
Menu
Back to cohort

Comparative transcriptomic analysis of nasopharyngeal swabs from individuals with and without COVID-19

2023· dataset· en· W6920828384 on OpenAlexaffabout

Bibliographic record

VenueFigshare · 2023
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTranscriptomeCohortCohort studyStatistical analysisOutpatient clinic

Abstract

fetched live from OpenAlex

This dataset contains the results of a transcriptomic (RNA-seq) analysis of nasopharyngeal swabs collected from 50 individuals with COVID-19 and 13 individuals without COVID-19 based on clinical qPCR testing. COVID-19 positive individuals included 16 outpatients, 16 hospitalized, non-ICU patients, and 18 hospitalized ICU patients. Samples were collected from a clinical cohort in Greater Toronto Area hospitals and outpatient assessment centres between October 2020 and October 2021. Four comparative analyses were performed using DESeq2: all COVID-19 positive individual versus negatives, COVID-19 ICU individuals versus negatives, non-ICU COVID-19 positive individuals versus negatives, and COVID-19 positive outpatients versus negatives. The analysis was performed with DESeq2 v1.30.1 using the Gencode human reference transcriptome v37.<br>

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 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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.071
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0880.016

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 head0.113
GPT teacher head0.364
Teacher spread0.250 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations1
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueFigshareFrench-language works237,207