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Growth Hormone Therapy in Heart Failure: Where Are We Now?

2003· review· en· W1979222623 on OpenAlexaff
Catherine Demers, Robert S. McKelvie

Bibliographic record

VenueCongestive Heart Failure · 2003
Typereview
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsHamilton Health SciencesHamilton General Hospital
Fundersnot available
KeywordsHeart failureMedicineQuality of life (healthcare)Cardiac function curveCardiologyInternal medicineIntensive care medicineClinical trialRandomized controlled trialHormone

Abstract

fetched live from OpenAlex

Despite improvement in survival with angiotensin-converting enzyme inhibitors and beta blockers, clinical events for patients with heart failure remain elevated. New therapies for heart failure are needed to improve functional capacity, quality of life, and prognosis. Growth hormone exerts direct and indirect effects on cardiac structure and function. Experimental models of heart failure and small studies have demonstrated significant improvements in cardiac function, hemodynamic parameters, functional capacity, and quality of life. Despite the lack of benefit demonstrated in small, short-term, randomized clinical trials, further studies are needed to assess the potential role of this adjuvant therapy in heart failure patients.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.046
GPT teacher head0.318
Teacher spread0.272 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations6
Published2003
Admission routes1
Has abstractyes

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