Exploring the Impact of Loin Pain in IgA Nephropathy: A United Kingdom-Wide Mixed-Methods Qualitative Study and Pilot Survey
Bibliographic record
Abstract
Background: Loin pain (LP) is a commonly reported symptom by patients with IgA nephropathy (IgAN). Currently there is little known about the frequency of LP, the impact of LP on quality of life or appropriate strategies to manage LP in IgAN. Methods: An online pilot survey was developed with input from IgAN patients and the National Registry of Rare Kidney Diseases (RaDaR). The survey recorded demographic, kidney disease, and LP parameters, alongside The Kidney Symptom Score, Short-Form McGill Pain Questionnaire-2, Pain Self-Efficacy Questionnaire, and Brief Illness Perception Questionnaire. A separate semi-structured interview study with patients, carers and healthcare professionals (HCPs) explored experiences of loin pain through thematic analysis. Results: 366 patient responses were analysed. 261 (71.1%) experienced LP; 28% currently, 33% in the last month, and 39.1% in the past. 70.7% experienced LP at least monthly. 80.5% reported LP as always the same, with ‘aching’ being the most common descriptor used. LP was associated with a poorer self-perception of mental and physical health, and strongly associated with the presence of systemic symptoms including itching, anorexia, and loss of libido. Only 41.6% could manage their LP with analgesics. 44.7% spoke to HCPs about LP, but 61.7% did not find this helpful. LP affected mobility in 43.9%. 48 interviews were conducted; 21 patients, 14 carers, and 13 HCPs (27% male, 73% female, 70% White British, age range 23-75 years). Five themes were developed in the thematic coding process; 1) ‘Achy pain’ in the ‘kidney area’; 2) Managing LP with limited options; 3) LP as a warning: anxiety of declining kidney function; 4) Impact to daily life dependent on severity; 5) Differing opinions between patients and healthcare professionals. Conclusion: Loin pain is a common symptom in patients with IgAN, affecting the majority of respondents. It has a negative impact on quality of life and self-perception of physical and mental health. Despite this, options to manage LP in IgAN remain limited, with HCPs having little knowledge or insight into optimal management strategies. Further work is being undertaken to explore these themes in other kidney diseases. Funding: Commercial Support - Omeros
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".