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Record W4214553879 · doi:10.1177/2333794x221078698

Association of Clinical Signs, Host Biomarkers and Etiology With Radiological Pneumonia in Bhutanese Children

2022· article· en· W4214553879 on OpenAlexaff
Sophie Jullien, Melissa Richard‐Greenblatt, Aina Casellas, Kinley Tshering, Jose Luis Ribó, Ragunath Sharma, Tshering Tashi, Dinesh Pradhan, Kumbu Dema, Michelle Ngai, Carmen Muñoz‐Almagro, Kevin C. Kain, Quique Bassat

Bibliographic record

VenueGlobal Pediatric Health · 2022
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsUniversity of TorontoToronto General HospitalUniversity Health Network
Fundersnot available
KeywordsMedicinePneumoniaProcalcitoninRadiological weaponEtiologyErythrocyte sedimentation rateClinical endpointIntensive care medicineInternal medicineSepsisRadiologyClinical trial

Abstract

fetched live from OpenAlex

Diagnosing pneumonia and identifying those requiring antibiotherapy remain challenging. Chest radiographs (CXR) are often used as the reference standard. We aimed to describe clinical characteristics, host-response biomarkers and etiology, and assess their relationship to CXR findings in children with pneumonia in Thimphu, Bhutan. Children between 2 and 59 months hospitalized with WHO-defined pneumonia were prospectively enrolled and classified into radiological endpoint and non-endpoint pneumonia. Blood and nasopharyngeal washing were collected for microbiological analyses and plasma levels of 11 host-response biomarkers were measured. Among 149 children with readable CXR, 39 (26.2%) presented with endpoint pneumonia. Identification of respiratory viruses was common, with no significant differences by radiological outcomes. No clinical sign was suggestive of radiological pneumonia, but children with radiological pneumonia presented higher erythrocyte sedimentation rate, C-reactive protein and procalcitonin. Markers of endothelial and immune activation had little accuracy for the reliable identification of radiological pneumonia.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.342
Teacher spread0.323 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations3
Published2022
Admission routes1
Has abstractyes

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