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DAILY HEMODIALYSIS—SELECTED TOPICS: Sleep Apnea and Daytime Sleepiness in End‐Stage Renal Disease

2004· review· en· W1905339237 on OpenAlexaff
Patrick J. Hanly

Bibliographic record

VenueSeminars in Dialysis · 2004
Typereview
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineEnd stage renal diseaseSleep apneaHemodialysisDialysisExcessive daytime sleepinessPopulationSleep disorderObstructive sleep apneaDiseaseCentral sleep apneaInternal medicineRestless legs syndromeIntensive care medicinePolysomnographyPhysical therapyApneaInsomniaPsychiatry

Abstract

fetched live from OpenAlex

Sleep disorders are common in patients with end-stage renal disease (ESRD). The prevalence of sleep apnea is 10 times greater in patients with ESRD than in the general population. Although sleep apnea is not improved by conventional modes of dialysis, it is corrected by nocturnal hemodialysis, which provides a new and unique model to study its pathophysiology in this patient population. In addition to causing sleep disruption and impairment of daytime function, sleep apnea may also increase the cardiovascular morbidity and mortality that is commonly found in patients with ESRD. "Pathological" daytime sleepiness is found in 50% of patients with ESRD. Although its pathogenesis has been related both to sleep apnea and periodic limb movements, it has also been attributed to a variety of metabolic factors, including the severity of uremia. Further research is required to evaluate the impact of sleep disorders on the clinical outcome of patients with ESRD.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.020
GPT teacher head0.319
Teacher spread0.299 · 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 designNot applicable
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

Citations106
Published2004
Admission routes1
Has abstractyes

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