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Record W2098534796 · doi:10.1586/14787210.3.2.251

Overview of antiviral and anti-inflammatory treatment for severe acute respiratory syndrome

2005· review· en· W2098534796 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

VenueExpert Review of Anti-infective Therapy · 2005
Typereview
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsNorth York General HospitalWestern University
Fundersnot available
KeywordsOutbreakMedicineMiddle East respiratory syndromeIntensive care medicineSurpriseDiseasePublic healthMainland ChinaVirusCoronavirus disease 2019 (COVID-19)ImmunologyVirologyInfectious disease (medical specialty)ChinaInternal medicinePathologyPolitical science

Abstract

fetched live from OpenAlex

In 2003, an outbreak of a novel respiratory virus exploded from mainland China into an international issue, catching the world by surprise. The ensuing challenges to hospital and public health workers rose to a level never before seen in healthcare, in part due to the unknown nature of the disease, the fear of the human-to-human transmission and the significant media involvement. A new coronavirus was identified as the causative agent and named the severe acute respiratory syndrome-associated virus. A number of antiviral and anti-inflammatory treatment strategies were explored during the epidemic, with varying success. Following the epidemic, in vitro antiviral analyses of numerous compounds have been conducted. This review summarizes treatment agents assessed during and after the 2003 severe acute respiratory syndrome outbreak, with the aim of guiding future decision makers should the virus return.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.888
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.105
GPT teacher head0.453
Teacher spread0.348 · 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