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Record W2207327073 · doi:10.1093/brain/awv272

Ethical considerations in functional magnetic resonance imaging research in acutely comatose patients

2015· article· en· W2207327073 on OpenAlexafffund
Charles Weijer, Tommaso Bruni, Teneille Gofton, G. Bryan Young, Loretta Norton, Andrew Peterson, Adrian M. Owen

Bibliographic record

VenueBrain · 2015
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsWestern University
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsMagnetic resonance imagingFunctional magnetic resonance imagingComa (optics)MedicineEthical issuesPsychologyIntensive care medicineNeuroscienceRadiologyEngineering ethics

Abstract

fetched live from OpenAlex

Determining the prognosis of comatose patients is difficult, and yet this information is vital for informing treatment decisions. While fMRI offers promise as a prognostic tool, further research is needed into its use. Weijer et al. provide the first analysis of the ethical issues surrounding such research in comatose patients.

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)0.369
metaresearch head score (Gemma)0.437
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.779

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3690.437
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0100.030
Scholarly communication0.0120.007
Open science0.0040.007
Research integrity0.0150.016
Insufficient payload (model declined to judge)0.0030.001

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 head0.274
GPT teacher head0.438
Teacher spread0.164 · 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.

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

Citations44
Published2015
Admission routes2
Has abstractyes

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