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Record W2163556672 · doi:10.1530/erp-15-0006

Dobutamine stress echocardiography after cardiac transplantation: implications of donor–recipient age difference

2015· article· en· W2163556672 on OpenAlexaff
Patrick H. Gibson, Fernando Riesgo, Jonathan Choy, Daniel H. Kim, Harald Becher

Bibliographic record

VenueEcho Research and Practice · 2015
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsUniversity of Alberta HospitalAlberta Hospital Edmonton
Fundersnot available
KeywordsDobutamineMedicineDemographicsHeart transplantationTransplantationCardiologyCardiac allograft vasculopathySignificant differenceInternal medicineHemodynamics

Abstract

fetched live from OpenAlex

Dobutamine stress echocardiography (DSE) is widely used during follow-up after cardiac transplant for the diagnosis of allograft vasculopathy. We investigated the effect of donor-recipient age difference on the ability to reach target heart rate (HR) during DSE. All cardiac transplant patients who were undergoing DSE over a 3-year period in a single institution were reviewed. Target HR was specified as 85%×(220 - patient age). Further patient and donor demographics were obtained from the local transplant database. 61 patients (45 male, 55±12 years) were stressed with a median dose of 40 mcg/kg per min dobutamine. Only 37 patients (61%) achieved target HR. Donor hearts were mostly younger (mean 41±14 years, P<0.001), with only 11 patients (18%) having donors who were older than they were. Patients with older donors required higher doses of dobutamine (median 50 vs 30 mcg/kg per min, P<0.001) but achieved a lower percentage target HR (mean 93% vs 101%, P=0.003) than those with younger donors did. Patients with older donors were less likely to achieve target HR (18% vs 67%, P=0.003). In conclusion, donor-recipient age difference affects the likelihood of achieving target HR and should be considered when a patient is consistently unable to achieve 'adequate' stress according to the patient's age.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.056
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.162
GPT teacher head0.456
Teacher spread0.293 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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