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Record W3043756684 · doi:10.3399/bjgp20x711797

Cardiopulmonary resuscitation in primary and community care during the COVID-19 pandemic

2020· editorial· en· W3043756684 on OpenAlexaff
Kamlesh Khunti, Sebastian Straube, Anil Adisesh, Xin Hui S Chan, Amitava Banerjee, Trisha Greenhalgh

Bibliographic record

VenueBritish Journal of General Practice · 2020
Typeeditorial
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsSt. Michael's HospitalUniversity of Alberta
FundersNIHR Leicester Biomedical Research CentreUniversity of LeicesterEconomic and Social Research CouncilNational Institute for Health and Care ResearchDepartment of Health and Social CareWellcome Trust
KeywordsMedicinePersonal protective equipmentCardiopulmonary resuscitationPandemicIntubationIntensive care medicineHealth careMedical emergencyMechanical ventilationEmergency medicineCoronavirus disease 2019 (COVID-19)ResuscitationDiseaseAnesthesiaInternal medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19), can be spread by droplets or aerosols, particularly through direct or close contact and aerosol generating procedures (AGPs).1 Supplies of personal protective equipment (PPE)2 are limited, raising uncertainties in clinical judgement about the balance between benefit (to the patient) and risk (to the healthcare worker) during medical procedures, such as cardiopulmonary resuscitation (CPR) undertaken without adequate protection during the COVID-19 pandemic. Lack of PPE has caused intense anxiety in view of the increased number of deaths in healthcare workers including in primary and community care.2 CPR can be a complex intervention comprising airway management, ventilation, chest compressions, drug therapy, and defibrillation.3 While the intubation component of CPR is almost universally classified as an AGP, there is controversy around the risk of chest compression (to the person performing it, and to other staff and bystanders).4 Risks to healthcare workers will vary depending on the setting where such individuals work (primary or community care versus hospital-based care); and whether the individual works in an environment where …

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)0.003
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.237
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.005
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 head0.020
GPT teacher head0.315
Teacher spread0.295 · 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
GenreEditorial

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

Citations1
Published2020
Admission routes1
Has abstractyes

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