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Record W2003657467 · doi:10.1159/000185911

Clinical and Psychological Correlates of Somatic Symptoms in Patients on Dialysis

2008· article· en· W2003657467 on OpenAlexaff
Brendan J. Barrett, H Vavasour, Alison Major, Patrick S. Parfrey

Bibliographic record

Venue˜The œNephron journals/Nephron journals · 2008
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsHealth Sciences CentreMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineNauseaAffect (linguistics)DialysisSleep disorderQuality of life (healthcare)Internal medicineJoint painDepression (economics)Peritoneal dialysisLogistic regressionPhysical therapyInsomniaPsychiatry

Abstract

fetched live from OpenAlex

Somatic symptoms are common in patients on dialysis. Their causes are largely unknown and their therapy is unsatisfactory. To examine the relationship of psychological and clinical factors to these symptoms, 191 interviews were done in patients on hemo- and peritoneal dialysis. The severity of 8 somatic symptoms (tiredness, sleep disturbance, cramps, pruritus, headache, nausea, dyspnea, joint pain) of importance in dialysis patients was measured using previously validated scales. Indices of affect and quality of life were obtained, as was demographic, clinical and laboratory information. The severity of each symptom was significantly related to the indices of affect and quality of life. Using multiple logistic regression, poor affect score was the strongest correlate of each of the following somatic symptoms, tiredness, pruritus, sleep disturbance and cramps. It was ahead of any clinical or demographic variable and was also significantly correlated with the severity of the other symptoms. Indices of hyperparathyroidism were significantly associated with headache, joint pain, dyspnea and nausea. We conclude that the strongest correlate of common somatic symptoms in dialysis patients is affect disturbance, and that therapy aimed at improving the affect may improve the symptoms.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.051
GPT teacher head0.350
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 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

Citations79
Published2008
Admission routes1
Has abstractyes

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