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Record W2536737334 · doi:10.1097/wad.0000000000000174

Willingness to Be a Brain Donor

2016· article· en· W2536737334 on OpenAlexaff
Linda Boise, Ladson Hinton, Howard J. Rosen, Mary C. Ruhl, Hiroko H. Dodge, Nora Mattek, Marilyn Albert, Andrea Denny, Joshua D. Grill, Travonia Hughes, Jennifer H. Lingler, Darby Morhardt, Francine Parfitt, Susan Peterson-Hazan, Viorela Pop, Tara Rose, Raj C. Shah

Bibliographic record

VenueAlzheimer Disease & Associated Disorders · 2016
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsInstitute of Aging
FundersNational Center for Advancing Translational SciencesNational Institute on Aging
KeywordsEthnic groupDonationAfrican americanDiseaseRace (biology)PsychologyGerontologyMedicineClinical psychologyFamily medicinePathology

Abstract

fetched live from OpenAlex

INTRODUCTION: Racial and ethnic groups are under-represented among research subjects who assent to brain donation in Alzheimer disease research studies. There has been little research on this important topic. Although there are some studies that have investigated the barriers to brain donation among African American study volunteers, there is no known research on the factors that influence whether or not Asians or Latinos are willing to donate their brains for research. METHODS: African American, Caucasian, Asian, and Latino research volunteers were surveyed at 15 Alzheimer Disease Centers to identify predictors of willingness to assent to brain donation. RESULTS: Positive predictors included older age, Latino ethnicity, understanding of how the brain is used by researchers, and understanding of what participants need to do to ensure that their brain will be donated. Negative predictors included African/African American race, belief that the body should remain whole at burial, and concern that researchers might not be respectful of the body during autopsy. DISCUSSION: The predictive factors identified in this study may be useful for researchers seeking to increase participation of diverse ethnic groups in brain donation.

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 score0.302
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.274
Teacher spread0.256 · 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

Citations47
Published2016
Admission routes1
Has abstractyes

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