MétaCan
Menu
Back to cohort
Record W4386955862 · doi:10.1186/s12882-023-03330-y

Health related quality of life during dialysis modality transitions: a qualitative study

2023· article· en· W4386955862 on OpenAlexaff
Chance S. Dumaine, Danielle E. Fox, Pietro Ravani, Maria Santana, Jennifer M. MacRae

Bibliographic record

VenueBMC Nephrology · 2023
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of CalgaryUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineDialysisNephrologyKidney diseaseQuality of life (healthcare)Peritoneal dialysisModality (human–computer interaction)ModalitiesQualitative researchTreatment modalityIntensive care medicinePhysical therapyGerontologyInternal medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Modality transitions represent a period of significant change that can impact health related quality of life (HRQoL). We explored the HRQoL of adults transitioning to new or different dialysis modalities. METHODS: We recruited eligible adults (≥ 18) transitioning to dialysis from pre-dialysis or undertaking a dialysis modality change between July and September 2017. Nineteen participants (9 incident and 10 prevalent dialysis patients) completed the KDQOL-36 survey at time of transition and three months later. Fifteen participants undertook a semi-structured interview at three months. Qualitative data were thematically analyzed. RESULTS: Four themes and five sub-themes were identified: adapting to new circumstances (tackling change, accepting change), adjusting together, trading off, and challenges of chronicity (the impact of dialysis, living with a complex disease, planning with uncertainty). From the first day of dialysis treatment to the third month on a new dialysis therapy, all five HRQoL domains from the KDQOL-36 (symptoms, effects, burden, overall PCS, and overall MCS) improved in our sample (i.e., those who remained on the modality). CONCLUSIONS: Dialysis transitions negatively impact the HRQoL of people with kidney disease in various ways. Future work should focus on how to best support people during this time.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.736
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.076
GPT teacher head0.389
Teacher spread0.313 · 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

Citations14
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueBMC NephrologySame topicDialysis and Renal Disease ManagementFrench-language works237,207