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Record W3027746899 · doi:10.37616/2212-5043.1038

Saudi Arabian Society Of Echocardiography Recommendations For Echocardiography Service During Corona Virus Disease 2019 (COVID-19) Outbreak

2020· article· en· W3027746899 on OpenAlexaff
Sami Ghazal, Fatima Qaddoura, Abdulhalim Kinsara Kinsara, Ahmed Omran, Merna Atiyah, Mustafa Al Refae, Faisal Dalak, Saeed Al Ahmari, Abdullah Al Sehly, Noureldin Sahal, Ahmed Onazi, Rima S. Bader

Bibliographic record

VenueJournal of the Saudi Heart Association · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineCoronavirus disease 2019 (COVID-19)PandemicOutbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakMedical emergencyCardiologyIntensive care medicineRadiologyDiseaseInternal medicineEmergency medicineVirologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

We are summarizing the recommendations for the use of Echocardiography in patients during COVID-19 pandemic. The patient risk for COVID-19 should be assessed according to the Saudi CDC guidelines. Echocardiography should only be performed of considered appropriate and will likely alter the clinical decision. In COVID-19 suspected/confirmed patients, echocardiography study should be performed bedside and in infection control approved area with airborne precaution. Limited focused imaging is recommended to minimize contact time. A dedicated machine for COVID-19 suspected/confirmed cases is recommended. Transesophageal echocardiography is considered an aerosol generating procedure; therefore, an alternative modality should be strongly considered. In COVID-19 suspected/confirmed patients, a transesophageal echocardiogram should be done only under strict airborne precaution. In low risk patient for COVID-19, Transesophageal echocardiography should be done with a minimum of droplet precaution, however; N95 respirator is preferred to surgical mask in this situation.

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)0.002
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: none
Teacher disagreement score0.629
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.059
GPT teacher head0.359
Teacher spread0.300 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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