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Record W2144706666 · doi:10.1186/1747-5341-8-15

Neuroethics, confidentiality, and a cultural imperative in early onset Alzheimer disease: a case study with a First Nation population

2013· article· en· W2144706666 on OpenAlexafffund
Shaun Stevenson, B. Lynn Beattie, Richard Vedan, Emily Dwosh, Lindsey Bruce, Judy Illes

Bibliographic record

VenuePhilosophy Ethics and Humanities in Medicine · 2013
Typearticle
Languageen
FieldNeuroscience
TopicNeuroethics, Human Enhancement, Biomedical Innovations
Canadian institutionsAlzheimer Society of CanadaUniversity of British Columbia HospitalNeuroDevNetUniversity of British Columbia
FundersBritish Columbia Knowledge Development FundCanadian Institutes of Health ResearchVancouver Foundation
KeywordsNeuroethicsCultural neuroscienceEngineering ethicsConstructiveNeurosciencePsychologySociologyCognitive scienceCognitionComputer science

Abstract

fetched live from OpenAlex

The meaningful consideration of cultural practices, values and beliefs is a necessary component in the effective translation of advancements in neuroscience to clinical practice and public discourse. Society's immense investment in biomedical science and technology, in conjunction with an increasingly diverse socio-cultural landscape, necessitates the study of how potential discoveries in neurodegenerative diseases such as Alzheimer disease are perceived and utilized across cultures. Building on the work of neuroscientists, ethicists and philosophers, we argue that the growing field of neuroethics provides a pragmatic and constructive pathway to guide advancements in neuroscience in a manner that is culturally nuanced and relevant. Here we review a case study of one issue in culturally oriented neuroscience research where it is evident that traditional research ethics must be broadened and the values and needs of diverse populations considered for meaningful and relevant research practices. A global approach to neuroethics has the potential to furnish critical engagement with cultural considerations of advancements in neuroscience.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.266
GPT teacher head0.386
Teacher spread0.120 · 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 designTheoretical or conceptual
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

Citations16
Published2013
Admission routes2
Has abstractyes

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