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Record W4212861422 · doi:10.1111/bdi.12935

Brainstorming Session I‐Actigraphy in bipolar disorders: Which parameters and which analyses? Chair: Benicio Frey

2020· article· en· W4212861422 on OpenAlexaff
Anastasiya Slyepchenko, Allega, Leng, Minuzzi, Eltayebani, Skelly, Sassi, Soares, Kennedy, Bruno Étain, Scott, Bellivier, Keming Gao, �. �. Arnol'd, Prihoda, Quinones, Singh Singh, Schinagle, Conroy, D'Arcangelo, Calabrese, Bowden, Julia Sweet, Miller, Mcintyre, Guolan Gao

Bibliographic record

VenueBipolar Disorders · 2020
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsUniversity Health NetworkSt. Joseph’s Healthcare HamiltonMcMaster University
Fundersnot available
KeywordsSession (web analytics)ActigraphyPsychologyBrainstormingPhysical medicine and rehabilitationBipolar disorderClinical psychologyPsychiatryMedicineLithium (medication)World Wide WebComputer scienceArtificial intelligenceInsomnia

Abstract

fetched live from OpenAlex

Through the use of actigraphy, sleep-wake cycle and biological rhythm patterns have been increasingly investigated in mood disorders, such as bipolar disorders (BD). Increasingly, attention has been directed toward studying actigraphy-derived variables beyond sleep, particularly in BD and other mood disorders. Emerging methods of analyzing actigraphy data include investigating probabilities of transitioning between rest and active states, and investigating variables quantifying light exposure, which is measured by some actigraph models.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.293
Teacher spread0.264 · 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.

Study designObservational
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
Published2020
Admission routes1
Has abstractyes

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