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The case for Chronotherapy in COVID-19 induced Acute Respiratory Distress Syndrome (ARDS).

2020· dataset· en· W3041276741 on OpenAlexaff
Faleh Tamimi, Mohammad Abusamak, Bindu Akkanti, Zheng Chen, Seung Hee Yoo, Harry Karmouty Quintana

Bibliographic record

VenueAuthorea · 2020
Typedataset
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsMcGill University
Fundersnot available
KeywordsARDSCytokine stormChronotherapy (sleep phase)MedicineCoronavirus disease 2019 (COVID-19)Acute respiratory distressPandemicIntensive care medicineCoronavirusImmune systemDiseaseImmunologyCircadian rhythmLungInternal medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

COVID-19, the disease resulting from infection by a novel coronavirus: SARS-Cov2 that has rapidly spread since November 2019 leading to a global pandemic. SARS-Cov2 has infected over 2.8 million people and caused over 180,000 deaths worldwide. Although most cases are mild, a subset of patients develop a severe and atypical presentation of Acute Respiratory Distress Syndrome (ARDS) that is characterised by a cytokine release storm (CRS). Paradoxically, treatment with anti-inflammatory agents and immune regulators has been associated with worsening of ARDS. We hypothesize that the intrinsic circadian clock of the lung and the immune system may regulate individual components of CRS and thus chronotherapy may be used to effectively manage ARDS in COVID-19 patients.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.047
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.072
GPT teacher head0.350
Teacher spread0.277 · 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 teacher head, not a consensus.

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

Citations0
Published2020
Admission routes1
Has abstractyes

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