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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 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)0.009
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0030.004
Scholarly communication0.0040.012
Open science0.0040.006
Research integrity0.0070.017
Insufficient payload (model declined to judge)0.0050.002

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 source (direct Gemma or distilled Codex), 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

Citations0
Published2022
Admission routes2
Has abstractyes

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