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Record W2607281629 · doi:10.1017/cjn.2015.154

Neurology (Neurocritical Care/Neuro Trauma)

2015· article· en· W2607281629 on OpenAlexvenueno aff
PM Pana, Laura Hornby, Sam D. Shemie, Jeanne Teitelbaum, Sonny Dhanani

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2015
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsNeurointensive careContext (archaeology)MedicineElectroencephalographyCirculatory systemBrain functionNeurologyOrgan donationObservational studyIntensive care medicineAnesthesiaPsychologySurgeryCardiologyInternal medicineNeuroscienceTransplantationPsychiatry

Abstract

fetched live from OpenAlex

Background: Donation after circulatory death (DCD) can reduce organ transplant waiting times. When defining death using circulatory criteria, brain function is usually not assessed. Residual brain function and the state of consciousness at the time of circulatory arrest is unknown. We have an ethical responsibility to ensure the donor is free of pain and psychological distress. Methods: We performed a scoping review of the literature to determine the time intervals associated with the loss brain function after circulatory arrest. Results: A total of 1133 articles were reviewed and 38 were included in the review. In humans, 8 studies showed loss of EEG activity under 30 seconds. Four studies revealed loss of EEG between 39.6 and 66 seconds. Clinically, loss of consciousness was shown to occur between 4 and 21 seconds. In animals, 13 studies also revealed loss of EEG under 30 seconds. In four other animal studies, EEG was lost between 37 and 120 seconds. Conclusion: The time required to lose brain function varied according to clinical context and method by which this function is measured. Existing literature is scarce and limited to observational studies and case reports.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.004

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 head0.076
GPT teacher head0.332
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2015
Admission routes1
Has abstractyes

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