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Record W6924956904 · doi:10.17605/osf.io/fvj4e

Sleep Architecture Measured by Polysomnography in Patients with Traumatic Brain Injury: A Scoping Review

2021· other· en· W6924956904 on OpenAlexaff

Bibliographic record

VenueOSF Preprints (OSF Preprints) · 2021
Typeother
Languageen
FieldComputer Science
TopicComputational Physics and Python Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPolysomnographySleep architectureSleep (system call)Traumatic brain injuryGold standard (test)Quality of life (healthcare)Sleep Stages

Abstract

fetched live from OpenAlex

Sleep disturbances are commonly exhibited following traumatic brain injury (TBI) and are associated with cognitive, behavioural, and/or health-related quality of life impairments. Sleep architecture, the basic organization of sleep, is affected by TBI. Polysomnography (PSG) measures multiple sleep-related neurophysiological parameters and is considered to be the gold standard assessment tool. We are conducting a scoping review of PSG findings in order to understand the dimension of sleep disturbances faced by individuals with TBI.

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)0.003
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0100.009
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.264
Teacher spread0.251 · 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 designSystematic review
Domainnot available
GenreReview

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
Published2021
Admission routes1
Has abstractyes

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