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Record W2993911298 · doi:10.1016/j.cjco.2019.11.007

The Role of Ambulatory Heart Failure Clinics to Avoid Heart Failure Admissions

2019· article· en· W2993911298 on OpenAlexaff
Jessica He, Sean Balmain, Jeremy Kobulnik, A. Schofield, Susanna Mak

Bibliographic record

VenueCJC Open · 2019
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsMount Sinai HospitalUniversity of Toronto
FundersServier
KeywordsHeart failureAmbulatoryMedicineCardiologyMedical emergencyInternal medicineIntensive care medicineEmergency medicine

Abstract

fetched live from OpenAlex

BACKGROUND: There is a complex relationship between heart failure (HF) clinic services and health outcomes. We hypothesized that ambulatory clinic activity may be associated with both hospital admission and also with avoidance of admission. METHODS: A retrospective comparative cohort study was conducted examining activity in an ambulatory HF Clinic. Consecutive clinic visits in 2013 were recorded (n = 1728) and periods of high-intensity utilization (HIU) were identified (n = 128). A HIU period was defined by ≥2 consecutive clinic visits within 30 days, ending after 30 days passed without an additional clinic visit. For each HIU period identified, patient characteristics (n = 107) and all clinic visits (n = 324) were examined. HIU periods were then classified by association with hospital admission (±30 days). RESULTS: In 2013, 18.8% of all clinic visits occurred during HIU periods, involving 13.7% of the clinic population. Thirty-eight percent of HIU periods were associated with 62 total hospital admissions (±30 days), of which 58% (n = 36) were for a primary diagnosis of HF. In addition,17 HIU periods met criteria for admission avoided, and 7 HIU periods occurring after hospital discharge also met criteria for admission avoided. CONCLUSIONS: We identified periods of intensive ambulatory clinic activity dedicated to patients with high burdens of comorbidities and both HF and non-HF-related admissions. These periods were also associated with episodes of successful decongestion with oral diuretics, resulting in avoidance of admission. Identifying HF patients who can be treated successfully or who are likely to require admission may be helpful for allocating clinic resources.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.002

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 head0.015
GPT teacher head0.315
Teacher spread0.300 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations7
Published2019
Admission routes1
Has abstractyes

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