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Record W2086109770 · doi:10.1109/mpul.2013.2242014

Clinical ethical concerns in the implantation of brain-machine interfaces: Part II: Specific clinical and technical issues affecting ethical soundness

2013· article· en· W2086109770 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Pulse · 2013
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSoundnessAssertionArgument (complex analysis)Ethical issuesEngineering ethicsMedicinePsychologyComputer scienceEngineeringInternal medicine

Abstract

fetched live from OpenAlex

In our article, "Clinical Ethical Concerns in the Implantation of Brain-Machine Interfaces: Part I," published in the January/February issue of IEEE Pulse [1], we suggested that implantable brain-machine interfaces (BMIs) are ethically unsound in all but a handful of rare cases. This argument hinges on the invasiveness of the implantation surgery and the existence of effective noninvasive alternatives for most patients. In this article, we seek to prove this assertion by discussing complications that may invalidate the device and/or require additional surgery, and we present suggestions for how implantable BMIs can be made more ethical in the future.

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.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
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 head0.119
GPT teacher head0.419
Teacher spread0.300 · 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