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Record W4385754191 · doi:10.14745/ccdr.v49i78a07

Nirmatrelvir-ritonavir use among adults hospitalized with COVID-19 during the Omicron phase of the COVID-19 pandemic, Canadian Nosocomial Infection Surveillance Program

2023· article· en· W4385754191 on OpenAlexafffundvenueabout
Robyn Mitchell, Diane Lee, Linda Pelude, Jeannette Comeau, John Conly, Chelsey Ellis, Jennifer Ellison, John M. Embil, Gerald A. Evans, Lynn Johnston, Jennie Johnstone, Kevin Katz, Pamela Kibsey, Bonita E. Lee, Marie‐Astrid Lefebvre, Yves Longtin, Allison McGeer, Dominik Mertz, Jessica Minion, Stephanie Smith, Jocelyn A. Srigley, Kathryn N. Suh, Jen Tomlinson, Alice Wong, Nisha Thampi, Charles Frenette

Bibliographic record

VenueCanada Communicable Disease Report · 2023
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsRoyal University HospitalAlberta Hospital EdmontonSaskatchewan Health AuthorityHamilton Health SciencesMcMaster UniversityJewish General HospitalSaskatchewan HealthIzaak Walton Killam Health CentreStollery Children's HospitalUniversity of CalgaryRoyal Jubilee HospitalNorth York General HospitalChildren's Hospital of Eastern OntarioQueen Elizabeth II Health Sciences CentreOttawa HospitalUniversity of Alberta HospitalHealth Sciences CentreQueen's UniversityAlberta Health ServicesMoncton HospitalMcGill University Health CentrePublic Health Agency of Canada
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsRitonavirMedicinePandemicCoronavirus disease 2019 (COVID-19)ComorbidityIntensive care unitEmergency medicineDiseasePediatricsInternal medicineIntensive care medicineHuman immunodeficiency virus (HIV)Family medicineInfectious disease (medical specialty)Viral loadAntiretroviral therapy

Abstract

fetched live from OpenAlex

Background: Recent studies have demonstrated the effectiveness of nirmatrelvir-ritonavir in reducing the risk of progression to severe disease among outpatients with mild to moderate coronavirus disease 2019 (COVID-19); however, data are limited regarding the use and role of nirmatrelvir-ritonavir among hospitalized patients. This study describes the use and outcomes of nirmatrelvir-ritonavir among adults hospitalized with COVID-19 in a sentinel network of Canadian acute care hospitals during the Omicron variant phase of the pandemic. Methods: The Canadian Nosocomial Infection Surveillance Program conducts surveillance of hospitalized patients with COVID-19 in acute care hospitals across Canada. Demographic, clinical, treatment and 30-day outcome data were collected by chart review by trained infection control professionals using standardized questionnaires. Results: From January 1 to December 31, 2022, 13% (n=490/3,731) of adult patients (18 years of age and older) hospitalized with COVID-19 in 40 acute care hospitals received nirmatrelvir-ritonavir either at admission or during hospitalization. Most inpatients who received nirmatrelvir-ritonavir, 79% of whom were fully vaccinated, had at least one pre-existing comorbidity (97%) and were of advanced age (median=79 years). Few were admitted to an intensive care unit (2.3%) and among the 490 nirmatrelvir-ritonavir treated inpatients, there were 13 (2.7%) deaths attributable to COVID-19. Conclusion: These findings from a large sentinel network of Canadian acute-care hospitals suggest that nirmatrelvir-ritonavir is being used to treat adult COVID-19 patients at admission who are at risk of progression to severe disease or those who acquired COVID-19 in hospital. Additional research on the efficacy and indications for nirmatrelvir-ritonavir use in hospitalized patients is warranted to inform future policies and guidelines.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.178

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.043
GPT teacher head0.349
Teacher spread0.305 · 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 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

Citations2
Published2023
Admission routes4
Has abstractyes

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