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Record W2921494475 · doi:10.1016/j.pmedr.2019.100851

Routinely assessing patients' sleep health is time well spent

2019· article· en· W2921494475 on OpenAlexafffund
Jean‐Philippe Chaput, Judy Shiau

Bibliographic record

VenuePreventive Medicine Reports · 2019
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsMaple Leaf Medical ClinicAgricultural Research Institute of OntarioUniversity of Ottawa
FundersCHEO Research Institute
KeywordsAlertnessSleep (system call)MedicineSleep qualitySleep medicineSleep debtPsychiatryPhysical therapySleep disorderInsomniaComputer science

Abstract

fetched live from OpenAlex

Poor sleep health is common in today's society and contributes to a wide array of health problems, decreases productivity, and increases the risk of accidents. Key sleep characteristics that should be assessed by clinicians include sleep duration, sleep quality, sleep timing, daytime alertness, and the absence of a sleep disorder. Examples of questions to be used by busy clinicians to quickly assess a patient's sleep health are provided. It is hoped that sleep health will be given the same level of attention as diet and exercise in clinic.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.301
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0250.002

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 head0.011
GPT teacher head0.316
Teacher spread0.305 · 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; both teacher heads agree on what is shown here.

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

Citations18
Published2019
Admission routes2
Has abstractyes

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