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Record W4380624005 · doi:10.1093/ndt/gfad063c_5479

#5479 EXPLORING CANADIAN PATIENT EXPERIENCES OF LIVING WITH LUPUS NEPHRITIS

2023· article· en· W4380624005 on OpenAlexaffabout
Francesca S. Cardwell, Adrian Boucher, Megan R.W. Barber, Kim Cheema, Susan J. Elliott, Ann E. Clarke

Bibliographic record

VenueNephrology Dialysis Transplantation · 2023
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsUniversity of CalgaryUniversity of Waterloo
Fundersnot available
KeywordsMedicineThematic analysisLupus nephritisSystemic lupus erythematosusCohortQualitative researchGerontologyFamily medicineDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background and Aims Lupus nephritis (LN) is one of the most severe manifestations of SLE; however, the Canadian patient experience remains understudied. This research investigated patient experiences and perspectives of 1) LN diagnosis; 2) living with LN; and 3) LN healthcare and treatment. Method Patients aged ≥18 years with biopsy-proven pure or mixed ISN/RPS Class III, IV, or V LN and fulfilling the ACR 1997 or SLICC 2012 Classification Criteria for SLE were purposefully recruited from a Canadian lupus cohort to participate in semi-structured in-depth interviews. These were conducted virtually and transcribed verbatim for subsequent thematic analysis using NVivo Qualitative Data Analysis Software. Results Thirty patients with LN were interviewed; 86.7% were female, mean (SD) age was 42.1 (16.4) years, mean (SD) age at SLE diagnosis was 29.8 (15.3) years (Table 1). Patients reported challenges seeking, receiving and adjusting to the LN diagnosis, and all described emotional impacts associated with diagnosis. Most patients had limited knowledge of LN prior to diagnosis, and this lack of understanding made it difficult to contextualise their illness (Figure 1). Patients reported both emotional wellbeing and physical health impacts associated with living with LN. While most have continued in paid employment, patients identified altered career aspirations, role changes, and the need for accommodations in the workplace. Patients also described modified leisure and social activities (Figure 1). While many identified supportive friends and family, a lack of nuanced understanding of their experiences by others was reported. LN was described as a factor in family planning considerations amongst those of childbearing age. Specifically, fear of changing LN medications and their reproductive side effects, fear of experiencing a flare while pregnant/when parenting, and concerns about passing on LN/other autoimmune conditions to their children emerged (Figure 1). Numerous aspects of LN management present challenges for patients, including visiting a range of healthcare providers, taking medication, monitoring diet, stress management and ensuring adequate rest. While many reported successful LN management with medication, others expressed concern with cost and side effects. The challenges associated with a lack of LN-specific information and resources were identified (Figure 1). Conclusion A lack of individual and public understanding of LN coupled with the uncertainties of diagnosis/living with LN create a substantial psychosocial burden as patients negotiate acceptable risk in the face of uncertainty (e.g., in family planning, treatment, paid employment, leisure). Results emphasize the need for wider LN awareness and will inform the development of LN-specific patient resources to increase understanding of LN and better support decision-making.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.359

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0170.006
Scholarly communication0.0040.001
Open science0.0020.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.028
GPT teacher head0.261
Teacher spread0.233 · 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 designQualitative
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
Published2023
Admission routes2
Has abstractyes

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