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Navigating the Crossroads of Coronary Artery Disease and Heart Failure

2006· review· en· W2148523301 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCirculation · 2006
Typereview
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsMontreal Heart InstituteMount Sinai Hospital
Fundersnot available
KeywordsMedicineCoronary artery diseaseHeart failureCardiologyDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Chronic heart failure (HF) affects 5 million patients in theUnited States and is responsible for 1 million hospi-talizations and 300 000 deaths annually.1 The total annual costs associated with this disorder have been estimated to exceed $40 billion.1,2 Chronic HF is the only category of cardiovascular diseases for which the prevalence, incidence, hospitalization rate, total burden of mortality, and costs have increased in the past 25 years.1,2 Fueling this epidemic is the increasing number of elderly patients developing impaired left ventricular (LV) function as a manifestation of chronic coronary artery disease (CAD).1,2 With the aging of the population and decline in mortality of other forms of cardio-vascular diseases, it is likely that the incidence of HF and its impact on public health will continue to increase.1–3 CAD and HF: Epidemiology and Prognosis In the past 3 decades, considerable attention has focused on

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.929
Threshold uncertainty score0.416

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.027
GPT teacher head0.309
Teacher spread0.282 · 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