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Record W6945347565 · doi:10.25384/sage.c.7150327.v1

The Association Between Intradialytic Symptom Clusters and Recovery Time in Patients Undergoing Maintenance Hemodialysis: An Exploratory Analysis

2024· other· en· W6945347565 on OpenAlexaboutno aff

Bibliographic record

VenueSage Journals Data · 2024
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsnot available
Fundersnot available
KeywordsHemodialysisQuality of life (healthcare)DialysisFeelingKidney diseaseCohortCohort studyProspective cohort studyCluster (spacecraft)Blood pressure

Abstract

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Background:Individuals receiving hemodialysis often experience concurrent symptoms during treatment and frequently report feeling unwell after dialysis. The degree to which intradialytic symptoms are related, and which specific symptoms may impair health-related quality of life (HRQoL) is uncertain.Objectives:To explore intradialytic symptoms clusters, and the relationship between intradialytic symptom clusters with dialysis treatment recovery time and HRQoL.Design/setting:We conducted a post hoc analysis of a prospective cohort study of 118 prevalent patients receiving hemodialysis in two centers in Calgary, Alberta and Hamilton, Ontario, Canada.Participants:Adults receiving hemodialysis treatment for at least 3 months, not scheduled for a modality change within 6 weeks of study commencement, who could provide informed consent and were able to complete English questionnaires independently or with assistance.Methods:Participants self-reported the presence (1 = <i>none</i> to 5 = <i>very much</i>) of 10 symptoms during each dialysis treatment, the time it took to recover from each treatment, and weekly Kidney Disease Quality of Life 36-Item–Short Form (KDQoL-36) assessments. Principal component analysis identified clusters of intradialytic symptoms. Mixed-effects, ordinal and linear regression examined the association between symptom clusters and recovery time (categorized as 0, &gt;0 to 2, &gt;2 to 6, or &gt;6 hours), and the physical component and mental component scores (PCS and MCS) of the KDQoL-36.Results:One hundred sixteen participants completed 901 intradialytic symptom questionnaires. The most common symptom was lack of energy (56% of treatments). Two intradialytic symptom clusters explained 39% of the total variance of available symptom data. The first cluster included bone or joint pain, muscle cramps, muscle soreness, feeling nervous, and lack of energy. The second cluster included nausea/vomiting, diarrhea and chest pain, and headache. The first cluster (median score: −0.56, 25th to 75th percentile: −1.18 to 0.55) was independently associated with longer recovery time (odds ratio [OR] 1.62 per unit difference in score, 95% confidence interval [CI]: 1.23-2.12) and decreased PCS (−0.72 per unit difference in score, 95% CI: −1.29 to −0.15) and MCS scores (−0.82 per unit difference in score, 95% CI: −1.48 to −0.16), whereas the second cluster was not (OR 1.24, 95% CI: 0.97-1.58; PCS 0.19, 95% CI −0.46 to 0.83; MCS −0.72, 95% CI: −1.50 to 0.06).Limitations:This was an exploratory analysis of a small data set from 2 centers. Further work is needed to externally validate these findings to confirm intradialytic symptom clusters and the generalizability of our findings.Conclusions:Intradialytic symptoms are correlated. The presence of select intradialytic symptoms may prolong the time it takes for a patient to recover from a dialysis treatment and impair HRQoL.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.802

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.017
GPT teacher head0.246
Teacher spread0.229 · 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 designNot applicable
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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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