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Record W3151825963 · doi:10.1016/j.cjco.2021.03.010

A 2020 Environmental Scan of Heart Failure Clinics in Ontario

2021· article· en· W3151825963 on OpenAlexafffundabout
Lakshmi Kugathasan, Troy Francis, Valeria E. Rac, Harindra C. Wijeysundera, Michael McDonald, Heather J. Ross, Ana Carolina Alba

Bibliographic record

VenueCJC Open · 2021
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsSunnybrook Health Science CentreToronto Public HealthHealth Sciences CentreUniversity Health Network
FundersUniversity Health Network
KeywordsMedicineFamily medicineEmergency medicineMedical emergency

Abstract

fetched live from OpenAlex

BACKGROUND: Multidisciplinary heart failure (HF) clinics decrease hospital admission rates and healthcare use, while improving patient outcomes. To understand the contemporary availability of HF clinics in Ontario, Canada, and the services provided, we performed an environmental scan of physician-led and nurse practitioner (NP)-led HF clinics. METHODS: Between November, 2019 and February 2020, we identified Ontario HF clinics led by physicians or NPs. Following an invitation, we conducted a semi-structured interview to evaluate the services offered and qualitatively compared our findings to the results of the 2010 Ontario provincial survey. RESULTS: <0.001) in both median annual and new patient visits. As previously reported, the clinics varied in services offered, but trended toward an increased availability of onsite echocardiography, exercise-stress testing, and nuclear cardiology. CONCLUSIONS: Compared to the survey performed a decade ago, the number of HF clinics and physicians have not changed, and the services provided remain heterogenous. However, the increased number of patients served suggests a greater demand for these clinics. Improving the accessibility of these clinics and standardizing the service model are critical to improving patient outcomes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.618
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0040.000

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 head0.021
GPT teacher head0.290
Teacher spread0.269 · 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.

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
Published2021
Admission routes3
Has abstractyes

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