Provider perspectives on treatment decision-making in nephrotic syndrome
Bibliographic record
Abstract
BACKGROUND: Managing patients with nephrotic syndrome (NS) remains difficult for the practicing nephrologist. This often young patient population is faced with a debilitating, relapsing and remitting disease with non-specific treatment options that are often poorly tolerated. Clinicians managing these complex patients must attempt to apply disease-specific evidence while considering the individual patient's clinical and personal situation. METHODS: We conducted qualitative interviews to ascertain the provider perspectives of NS, treatment options and factors that influence recommendations for disease management, and administered a survey to assess both facilitators and barriers to the implementation of the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. RESULTS: When making treatment recommendations, providers considered characteristics of various treatments such as efficacy, side effects and evaluation of risk versus benefit, taking into account how the specific treatment fit with the individual patient. Time constraints and the complexity of explaining the intricacies of NS were noted as significant barriers to care. Although the availability of guidelines was deemed a facilitator to care, the value of the KDIGO guidelines was limited by the perception of poor quality of evidence. CONCLUSIONS: The complexity of NS and the scarcity of robust evidence to support treatment recommendations are common challenges reported by nephrologists. Future development and use of shared learning platforms may support the integration of best available evidence, patient/family preferences and exchange of information at a pace that is unconstrained by the outpatient clinic schedule.
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".