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Record W2029728712 · doi:10.1377/hlthaff.2012.0895

Default Options In Advance Directives Influence How Patients Set Goals For End-Of-Life Care

2013· article· en· W2029728712 on OpenAlexaff
Scott D. Halpern, George Loewenstein, Kevin G. Volpp, Elizabeth Cooney, Kelly C. Vranas, Caroline M. Quill, Mary S. McKenzie, Michael O. Harhay, Nicole B. Gabler, Tatiana Silva, Robert M. Arnold, Derek C. Angus, Cindy L. Bryce

Bibliographic record

VenueHealth Affairs · 2013
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsInstitute of Health Economics
FundersNational Heart, Lung, and Blood Institute
KeywordsDirectiveReceiptEnd-of-life careDefaultActuarial scienceQuality of life (healthcare)Personal careTest (biology)Set (abstract data type)BusinessMedicinePsychologyPalliative careNursingFinanceFamily medicineAccountingComputer science

Abstract

fetched live from OpenAlex

Although decisions regarding end-of-life care are personal and important, they may be influenced by the ways in which options are presented. To test this hypothesis, we randomly assigned 132 seriously ill patients to complete one of three types of advance directives. Two types had end-of-life care options already checked-a default choice-but one of these favored comfort-oriented care, and the other, life-extending care. The third type was a standard advance directive with no options checked. We found that most patients preferred comfort-oriented care, but the defaults influenced those choices. For example, 77 percent of patients in the comfort-oriented group retained that choice, while 43 percent of those in the life-extending group rejected the default choice and selected comfort-oriented care instead. Among the standard advance directive group, 61 percent of patients selected comfort-oriented care. Our findings suggest that patients may not hold deep-seated preferences regarding end-of-life care. The findings provide motivation for future research examining whether using default options in advance directives may improve important outcomes, including patients' receipt of wanted and unwanted services, resource use, survival, and quality 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)0.001
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 score0.062
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.064
GPT teacher head0.409
Teacher spread0.345 · 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

Citations159
Published2013
Admission routes1
Has abstractyes

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