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Record W2555748643 · doi:10.1093/ndt/gfw172.28

SP488MYOCARDIAL STUNNING DURING RENAL REPLACEMENT THERAPY FOR ACUTE KIDNEY INJURY

2016· article· en· W2555748643 on OpenAlexaff
Huda Mahmoud, Christopher W. McIntyre

Bibliographic record

VenueNephrology Dialysis Transplantation · 2016
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug-Induced Hepatotoxicity and Protection
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineRenal replacement therapyAcute kidney injuryStunningKidneyInternal medicineIschemia

Abstract

fetched live from OpenAlex

Introduction and Aims: The circulatory stress of chronic haemodialysis (HD) results in repetitive subclinical myocardial ischaemia (myocardial stunning), which contributes to adverse patient outcomes. Currently it is unknown whether this process occurs during renal replacement therapy (RRT) for acute kidney injury (AKI). Acute RRT differs in both its delivery and because patients do not display the circulatory changes of chronic uraemia that increase ischaemic susceptibility. We therefore aimed to determine whether acute RRT is capable of inducing myocardial stunning. Methods: 12 patients requiring RRT for AKI participated in an observational study. Serial echocardiography was performed before, during and after a single RRT session and images analysed off-line using speckle-tracking software. Myocardial stunning was defined as ≥2 new left ventricular (LV) regional wall motion abnormalities (RWMAs) during dialysis (>20% reduction in regional longitudinal strain); we used global longitudinal strain (GLS) to assess left ventricular contractility. Blood pressure (BP) and systemic haemodynamics were measured continuously using thoracic bioreactance. Intra-dialytic hypotension (IDH) was defined as >20mmHg fall in systolic blood pressure (SBP) or SBP <90mmHg.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.053
GPT teacher head0.386
Teacher spread0.333 · 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 designBench or experimental
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

Citations1
Published2016
Admission routes1
Has abstractyes

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