MétaCan
Menu
Back to cohort
Record W4283072831 · doi:10.1016/j.cjco.2022.06.004

Patient Outcomes and Characteristics in a Contemporary Quaternary Canadian Cardiac Intensive Care Unit

2022· article· en· W4283072831 on OpenAlexafffundabout
Adriana Luk, Eduard Ródenas‐Alesina, Fernando Luís Scolari, Vicki N. Wang, Darshan H. Brahmbhatt, Alexandra G. Hillyer, Nikki Huebener, Nicole Fung, Madison Otsuki, Christopher B. Overgaard

Bibliographic record

VenueCJC Open · 2022
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsUniversity Health NetworkTed Rogers Centre for Heart ResearchSouthlake Regional Health CenterUniversity of Toronto
FundersUniversity Health NetworkHeart and Stroke Foundation of Canada
KeywordsCardiogenic shockMedicineCoronary care unitMyocardial infarctionIntensive care unitEmergency medicineHeart failurePopulationIntensive careMortality rateInternal medicineCardiologyIntensive care medicine

Abstract

fetched live from OpenAlex

Background: The modern-day cardiac intensive care unit (CICU) has evolved to care for patients with acute critical cardiac illness. We describe the current population of cardiac patients in a quaternary CICU. Methods: Consecutive CICU patients admitted to the CICU at the Toronto General Hospital from 2014 to 2020 were studied. Patient demographics, admission diagnosis, critical care resources, complications, in-hospital mortality, and CICU and hospital length of stay were recorded. Results: < 0.001). Conclusions: Given the trends of higher acuity of patients with cardiac critical illness, with higher use of critical care resources, education streams for critical care within cardiology, and alternative pathways of care for patients who have lower-acuity cardiac disease remain imperative to manage this evolving population.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.818
Threshold uncertainty score0.366

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.111
GPT teacher head0.348
Teacher spread0.237 · 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

Citations5
Published2022
Admission routes3
Has abstractyes

Explore more

Same venueCJC OpenSame topicSepsis Diagnosis and TreatmentFrench-language works237,207