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Record W4308727537 · doi:10.17975/sfj-2022-016

Improving diagnosis for disorders of consciousness: The case for a novel multi-paradigm approach

2022· article· en· W4308727537 on OpenAlexaffvenue
Yi An Wang, Adam M.R. Groh

Bibliographic record

VenueSTEM Fellowship Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsMcGill UniversityMontreal Neurological Institute and HospitalUniversity of Calgary
Fundersnot available
KeywordsMinimally conscious statePersistent vegetative stateWakefulnessConsciousness DisordersConsciousnessComa (optics)ArousalPsychologyLevel of consciousnessAltered stateCognitive psychologyNeuroscienceDevelopmental psychologyElectroencephalography

Abstract

fetched live from OpenAlex

After a traumatic brain injury, patients often experience a period of impaired consciousness characterized by a diminished ability to perceive external stimuli (i.e., awareness) and a diminished responsiveness to stimuli, when perceived (i.e., arousal) [1,2]. These impaired levels of consciousness are defined as disorders of consciousness (DoC), a spectrum typically defined by three states of consciousness: coma, unresponsive wakefulness syndrome (UWS, formerly known as vegetative state), and minimally conscious state (MCS) [1].

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.035
GPT teacher head0.288
Teacher spread0.254 · 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 designQualitative
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

Citations0
Published2022
Admission routes2
Has abstractyes

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