How to optimize the design and implementation of risk prediction tools: focus group with patients with IgA nephropathy
Bibliographic record
Abstract
BACKGROUND: IgA nephropathy (IgAN) is a common type of chronic immune-mediated kidney disease with variable risk of progression to end-stage kidney disease. Risk stratification helps clinicians weight the potential risks and benefits of immunosuppressive therapy for individual patients, and can inform patient-centred communication. No prior research examined barriers of risk predication tools (RPT) specific to IgAN. The purpose of this study was to explore determinants (facilitators, barriers) of RPT use from the patient perspective. METHODS: We conducted a single focus group with English-speaking adults aged 18 or older with biopsy-proven IgAN. We asked about how they would use an IgAN RPT, and how to improve its design and implementation. We analyzed the transcript using constant comparison to inductively derive themes, and complied with qualitative research reporting criteria. RESULTS: , and proteinuria ranged from 0.36 to 1.41 g/d. Participants identified both benefits and harms of the risk score. They said physicians should first ask patients for permission to use it. To make it more useful, participants offered suggestions to enhance RTP design: visual display, information on how to interpret the risk score, risk categories, health implications, modifiable risk factors, multiple scenarios, and comparison with similar patients. They offered additional suggestions to enhance RPT implementation: it should not replace patient-provider discussion, it should be accompanied by self-management education so that patients can take an active role in their health. Participants appreciated information from members of the multidisciplinary team in addition to physicians. Participants also said that physicians should monitor patient emotions or concerns on an ongoing basis. CONCLUSIONS: Patients with IgAN identified numerous ways to enhance the design and use of an RPT. Others could use this information to design and implement RPTs for patients with other conditions, but should employ user-centred design to develop RPTs that address patient preferences.
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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 | 0.045 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".