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Record W2200332843 · doi:10.5539/gjhs.v8n8p121

Sleep Quality and Depression and Their Association with Other Factors in Hemodialysis Patients

2015· article· en· W2200332843 on OpenAlexvenueno aff
Masomeh Norozi Firoz, Vida Shafipour, Hedayat Jafari, Seyed Hamzeh Hosseini, Jamshid Yazdani Charati

Bibliographic record

VenueGlobal Journal of Health Science · 2015
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
FundersMazandaran University of Medical Sciences
KeywordsHemodialysisMedicineDepression (economics)DialysisBeck Depression InventoryInternal medicinePittsburgh Sleep Quality IndexSleep qualityInsomniaPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Sleep disorders and depression, accompanied by reduced quality of life and increased mortality are the most common psychological problems in dialysis patients. This study was conducted with the aim to investigate depression and sleep quality and their association with some demographic and clinical factors in hemodialysis patients. METHOD: This descriptive-correlative study was conducted on 310 patients undergoing hemodialysis in 8 centers in educational hospitals in Mazandaran University of Medical Sciences. Data collection tools included a demographic questionnaire, Beck Depression Inventory, and Pittsburg Sleep Quality Index (PSQI). Statistical analysis was conducted using Chi-Square test and regression model. RESULTS: Results obtained showed 44.8% depression in patients. Significant relationships were found between depression and increased blood phosphorus (P=0.002) and urea (P=0.001). Poor sleep quality was observed in 73.5% of hemodialysis patients, which was found significantly related to aging (P=0.048), female (P=0.04), and reduced frequency of weekly hemodialysis (P=0.035). CONCLUSION: Depression and poor sleep quality are two common factors in hemodialysis patients, but patients do not overtly show symptoms of these disorders.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.125

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.032
GPT teacher head0.337
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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations31
Published2015
Admission routes1
Has abstractyes

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