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Abstract 13306: Advanced Care Planning in Persons Who Die From Heart Disease

2016· article· en· W4395039400 on OpenAlexaff
Joy R. Goebel, Olga Korosteleva, Elizabeth Ortega, Timothy Manning, Yemisrach T Lodebo

Bibliographic record

VenueCirculation · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsManning Diversified Forest Products (Canada)
Fundersnot available
KeywordsMedicineIntensive care medicineDiseaseGerontologyInternal medicine

Abstract

fetched live from OpenAlex

Introduction: The unpredictable trajectory of persons dying from cardiovascular disease (CVD) may make planning for future care challenging. In order to target quality improvement (QI) efforts, it is necessary to identify factors associated with advanced care planning (ACPing) in CVD. Methods: The Health Retirement Survey (a nationally representative sample) provided data for a secondary data analysis of 1304 individuals’ ≥65 years of age that died of CVD from 2002-2012. Proxies of decedents reported ACPing activities (identifying a durable power of attorney for health care [DPOA_HC], completing a living will, or engaging in a conversation about care preferences at end of life (EoL). Demographic and clinical factors were also recorded. Results: Proxies reported 78% of decedents engaged in some level of ACPing (60% had a DPOA_HC, 56.5% had a conversation about EoL treatment preferences, and 47.3% completed a living will). In bivariate analysis, being older, female, Caucasian, widowed, dying at home/hospice/other, having higher income or education, and more symptoms were associated with any type of ACPing (chi-squared test p-value ranges between <0.000 and 0.028). In the multivariate regression model, older age (OR=1.026, 95%CI [1.002, 1.052], p=0.037), Caucasian race (OR=1.754, 95%CI [1.126, 2.735], p=0.013), more education (‘high school grad’: OR=1.723, 95%CI [1.151, 2.580], p=0.008; ‘some college’: OR=2.659, 95% CI [1.451, 4.871], p=0.002; ‘college grad’: OR=3.404, 95%CI [1.710, 6.774], p<0.000), higher income (‘$10,001-$88,000’: OR=1.670, 95%CI [1.000, 2.791], p=0.050; ‘>$88,000’: OR=2.012, 95%CI [1.150,3.520], p=0.014), and more symptom burden (OR=1.350, 95%CI [1.217, 1.498], p<0.000) were associated with any type of ACPing. Conclusions: The majority of individuals dying of CVD participate in ACPing. QI efforts to improve ACPing should target non-Caucasian individuals with less education, less income, younger age, and lower symptom burden.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.115
GPT teacher head0.464
Teacher spread0.350 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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