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Record W2914661492 · doi:10.1177/0825859719829492

Delivery of End-of-Life Care in Patients Requesting Withdrawal of a Left Ventricular Assist Device Using Intranasal Opioids and Benzodiazepines

2019· article· en· W2914661492 on OpenAlexaff
Evan J. Wiens, Jana Pilkey, Jonathan K. Wong

Bibliographic record

VenueJournal of Palliative Care · 2019
Typearticle
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsDiscontinuationMedicinePalliative careMidazolamIntensive care medicineVentricular assist deviceEnd-of-life careInternal medicineHeart failureAnesthesiaNursing

Abstract

fetched live from OpenAlex

With the increasing prevalence of the left ventricular assist device (LVAD) in patients with end-stage cardiomyopathies, an increasing number of these patients are dying of noncardiac conditions. It is likely that the palliative care clinician will have an ever-increasing role in managing end of life for patients with LVADs, including discontinuation of LVAD support. There exists a paucity of literature describing strategies for effective delivery of palliative care in patients requesting discontinuation of LVAD therapy. Here, we present a case of a patient with metastatic cancer who requested LVAD discontinuation. Because of practical concerns and patient preference, the patient did not have intravenous (IV) access and medications requiring IV administration could not be used. Therefore, a strategy using intranasal midazolam and sufentanil was applied, the LVAD was deactivated, and the patient died comfortably. This case is, to our knowledge, the first to describe a strategy for delivery of palliative care in patients requesting discontinuation of LVAD support, particularly in the absence of IV access. Such a strategy may be applicable to patients wishing to die at home, and therefore allow greater latitude for patients and clinicians in their approach to the end of life.

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)0.000
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 score0.011
Threshold uncertainty score0.597

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.015
GPT teacher head0.252
Teacher spread0.237 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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