MétaCan
Menu
Back to cohort
Record W4229049176 · doi:10.1016/s2666-7568(22)00101-5

Omicron infection milder in nursing home residents

2022· letter· en· W4229049176 on OpenAlexaboutno aff
Morgan J. Katz, Robin Jump

Bibliographic record

VenueThe Lancet Healthy Longevity · 2022
Typeletter
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsnot available
FundersGeriatric Research Education and Clinical Center
KeywordsMedicineCohortHazard ratioProspective cohort studyCohort studyPediatricsFamily medicineEmergency medicineDemographyInternal medicineConfidence interval

Abstract

fetched live from OpenAlex

Nursing home staff and administrators are exhausted, overwhelmed, and ready for a break. Many have wondered if we will continue to see the devastation and tragedy of the early COVID-19 waves in nursing homes as each new variant emerges. A prospective cohort study published in The Lancet Healthy Longevity by Maria Krutikov and colleagues1 suggests that this might not be the case. The Article describes differences in hospital admissions and death during the delta versus omicron waves in nursing home residents enrolled in the VIVALDI cohort (ISRCTN 14447421) and suggests less risk of severe outcomes with the omicron variant.

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)0.002
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.078
GPT teacher head0.409
Teacher spread0.330 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueThe Lancet Healthy LongevitySame topicSARS-CoV-2 and COVID-19 ResearchFrench-language works237,207