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Radiology of Severe Acute Respiratory Syndrome (SARS)

2006· review· en· W1969046665 on OpenAlexaff
Loren H. Ketai, Narinder Paul, Ka-tak T. Wong

Bibliographic record

VenueJournal of Thoracic Imaging · 2006
Typereview
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsUniversity Health NetworkMount Sinai Hospital
Fundersnot available
KeywordsMedicineDiffuse alveolar damageBronchiolitisSevere acute respiratory syndromePathologyAir trappingPneumoniaLungAirwayARDSRespiratory systemRespiratory distressFibrosisReticular connective tissueRadiologyDiseaseCoronavirus disease 2019 (COVID-19)Acute respiratory distressInternal medicineSurgeryInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Severe acute respiratory distress syndrome (SARS) caused by SARS-associated coronavirus (SARS-CoV) is a systemic infection that clinically manifests as progressive pneumonia. During the initial phases of infection the virus causes pauci-inflammatory alveolar and interstitial edema that result in imaging abnormalities dominated by ground glass opacities (GGO). Severe SARS cases can develop radiologic and pathologic findings of diffuse alveolar damage. Although radiologic evidence of acute bronchiolitis is absent, SARS-CoV also infects ciliated airway epithelium, probably accounting for respiratory transmissibility of the virus. Radiologic recovery from SARS can be complete, but computed tomography images often show persistent GGO and reticular opacities, some of which reflect pathologic findings of fibrosis. Long-term follow-up imaging of survivors shows gradual decrease of GGO and reticulation with persistent air trapping in some patients. The latter is evidence of small airway disease that is not radiologically evident at the onset of the disease.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.005

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 head0.086
GPT teacher head0.476
Teacher spread0.390 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations84
Published2006
Admission routes1
Has abstractyes

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