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Record W3015863609 · doi:10.1007/s40620-020-00736-x

Reduced myocardial blood flow reserve in kidney transplant candidates may hamper risk stratification

2020· article· en· W3015863609 on OpenAlexaff
Majed Malak, Emily Christie, Kevin Wen, Haimanot Wasse, Morteza Hajihosseini, Padma Kaul, Paolo Raggi

Bibliographic record

VenueJournal of Nephrology · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineDipyridamoleCardiologyKidney diseaseInternal medicineHemodialysisCoronary artery diseaseDiabetes mellitusDialysisRenal functionEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: Rb-positron emission tomography-computed tomography can measure myocardial blood flow (MBF), the response to vasodilator stress can be verified rendering the results of the scan more reliable. METHODS: We reviewed the MBF response to dipyridamole infusion in 328 patients with end-stage kidney disease (ESKD) prior to transplant (188 hemodialysis-HD, 120 peritoneal dialysis-PD, and 20 pre-dialysis patients-CKD5) and in 100 controls with normal kidney function. A stress/rest MBF ratio ≥ 2 was considered an adequate response to dipyridamole. Coronary artery calcium (CAC) was measured on CT. RESULTS: Inadequate MBF response was seen in 36%-HD, 21%-PD, 45%-CKD5 vs. 23%-controls (p = 0.006). Univariable predictors of poor MBF response in ESKD patients were age, diabetes mellitus, and CAC (all p < 0.03) while serum hemoglobin was borderline significant (p = 0.052). Multivariable predictors of a poor MBF response were age (p = 0.002) and lower serum hemoglobin (p = 0.014). Ischemia was identified in 8% of ESKD patients and 24% of controls (p < 0.001). CONCLUSIONS: ESKD patients are less likely to respond appropriately to vasodilator stress compared to patients with normal renal function and had a lower incidence of ischemia despite a high pre-test probability of disease. Physicians performing vasodilator stress without MBF measurement should be aware of the high probability of a false negative response.

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)0.001
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 score0.230
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
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 head0.016
GPT teacher head0.266
Teacher spread0.250 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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