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Record W1973395387 · doi:10.1097/qco.0b013e328336eaae

Epidemic viral pneumonia

2010· review· en· W1973395387 on OpenAlexaff
Stephen E. Lapinsky

Bibliographic record

VenueCurrent Opinion in Infectious Diseases · 2010
Typereview
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMedicinePneumoniaVirologyCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Viral pneumoniaInternal medicineOutbreakInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Two recent viral epidemics producing pneumonitis (severe acute respiratory syndrome and pandemic influenza A H1N1) have highlighted the potential for viral infections to cause respiratory failure with a significant risk of mortality. This review describes these epidemics and other causes of epidemic viral pneumonia. RECENT FINDINGS: The recent literature highlights the rapidity with which these emerging viral infections can be characterized and how management strategies, including supportive care, antiviral therapy and infection control precautions, can be rapidly shared and implemented. SUMMARY: The severe acute respiratory syndrome outbreak was too short to allow management protocols to be tested in a research environment. The current 2009 influenza A (H1N1) pandemic is fortunately not associated with as high a mortality rate as the avian influenza A (H5N1), another potential pandemic candidate virus. Prior pandemic planning as well as research planning has allowed a rapid response to this outbreak, with a significant amount of literature generated in a few months. Other common seasonal viruses, such as respiratory syncytial virus and parainfluenza, as well as previously poorly recognized viruses such as hantavirus, have the ability to cause significant respiratory morbidity and mortality.

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), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.002

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 head0.133
GPT teacher head0.483
Teacher spread0.350 · 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
GenreReview

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

Citations23
Published2010
Admission routes1
Has abstractyes

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