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Record W2139434144 · doi:10.1017/s095026880600714x

Risk of hospitalization during influenza season among a cohort of patients with congestive heart failure

2006· article· en· W2139434144 on OpenAlexaff
Carmen Sandoval, Stephen D. Walter, Patrick M. Krueger, Marek Smieja, Andrew M. Smith, Salim Yusuf, Mark Loeb

Bibliographic record

VenueEpidemiology and Infection · 2006
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsMcMaster University
FundersCenters for Disease Control and Prevention
KeywordsMedicineHazard ratioHeart failureConfidence intervalInternal medicineProportional hazards modelCohortFlu seasonRetrospective cohort studyRelative riskCohort studyInfluenza seasonCardiologyEmergency medicineInfluenza vaccineVirologyVaccination

Abstract

fetched live from OpenAlex

It is uncertain whether hospitalization among patients with congestive heart failure (CHF) increases during the influenza season. This retrospective cohort study used influenza surveillance data from the United States (1986-1987 to 1990-1991), clinical information from the Studies of Left Ventricular Dysfunction (SOLVD) database, and daily temperature data from the National Climatic Data Center to assess the effect of influenza season on hospitalizations in this cohort of patients. The overall hospitalization rate was higher during influenza seasons compared to non-influenza seasons [relative risk (RR) 1.08, 95% confidence interval (CI) 1.01-1.16]. Multivariable Cox modelling revealed an adjusted hazard ratio (HR) of 1.11 for hospitalization during the influenza season (95% CI 1.03-1.20, P=0.005). Overall death rates were also higher during influenza seasons than non-influenza seasons (RR 1.09, 95% CI 0.97-1.21), but the corresponding adjusted HR for death was not significant (HR 1.01, 95% CI 0.98-1.24, P=0.11). Patients with CHF have a greater risk of hospitalization during the influenza season than in the non-influenza season, supporting the current belief that patients with CHF should be regarded as a high-risk group.

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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.311
Teacher spread0.294 · 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

Citations60
Published2006
Admission routes1
Has abstractyes

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