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Sleep and medical disorders

2008· book-chapter· en· W214669957 on OpenAlexaff
Andrea Iaboni, Harvey Moldofsky

Bibliographic record

VenueSleep Medicine · 2008
Typebook-chapter
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSleep (system call)Sleep medicineMedicineSleep disorderPsychiatryInsomniaComputer science

Abstract

fetched live from OpenAlex

This chapter describes both the impact of medical diseases and their treatments on sleep, and how disordered sleep can contribute to medical illnesses. Airway function has a normal circadian variation, with peak airflow in the afternoon and the lowest in the early morning. In people with asthma, this morning trough is associated with worsening of asthma symptoms and sleep disturbance. In individuals with heartburn at least once weekly, three-quarters complain of heartburn affecting their sleep. Polysomnography helps to clarify the nature and severity of the primary sleep disorder and aid in the management of end-stage renal disease (ESRD). Disruption of sleep is common in people with arthritic or muscular pain. Pain, sleep disturbance, and low mood are all believed to contribute to fatigue, a common complaint of those with rheumatic disorders. Circadian sleep/wakefulness is intricately linked to neuroendocrine and neuroimmune functions.

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), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.686
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0450.001

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 head0.012
GPT teacher head0.269
Teacher spread0.256 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2008
Admission routes1
Has abstractyes

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