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Record W3022359729 · doi:10.4081/monaldi.2020.1327

COVID-19 and clinical mimics. Correct diagnosis is the key to appropriate therapy

2020· article· en· W3022359729 on OpenAlex
Kamal Kant Sahu, Ajay Kumar Mishra, Kevin Martin, Iryana Chastain

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

VenueMonaldi Archives for Chest Disease · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsSaint-Vincent Hospital
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicineGlobeIntensive care medicineDiseaseHealth careMedical emergencyVirologyInfectious disease (medical specialty)Political sciencePathology

Abstract

fetched live from OpenAlex

As of 29 April 2020, across the globe, there are 3,216,353 confirmed Coronavirus disease 2019 (COVID-19 disease) with 227,894 deaths. The health care infrastructure of most of the countries is overwhelmed due to the gigantic upsurge of the new cases within a short time period. Most of the beds in the regular wards and critical care units are currently occupied by either people under investigation (PUI) or COVID-19 confirmed cases. We hereby discuss the challenges faced while approaching any case of shortness of breath, or other common upper respiratory symptoms during the current COVID-19 pandemic era.

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)0.069
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.555
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.069
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.124
GPT teacher head0.436
Teacher spread0.312 · 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