Perceptions of Prognostic Risks in Chronic Kidney Disease: A National Survey
Bibliographic record
Abstract
BACKGROUND: Predicting the clinical trajectories of chronic kidney disease (CKD) to discern personalized care remains a complex challenge in nephrology. Understanding the appropriate risk thresholds and time frame associated with predicting risks of key outcomes (kidney failure, cardiovascular (CV) events, and death) is critical in facilitating decision-making. As part of an exploratory research and practice support needs assessment, we aimed to determine the importance of the time frames for predicting key outcomes, and to assess the perceived demand for risk prediction tools among Canadian nephrologists. METHODS: A web-based survey was developed by a pan-Canadian expert panel of practitioners. Upon pre-test for clarity and ease of completion, the final survey was nationally deployed to Canadian nephrologists. Anonymous responses were gathered over a 4-month period. The results were analyzed using descriptive statistics. RESULTS: One hundred eleven nephrologists responded to our survey. The majority of the respondents described prediction of events over time frames of 1-5 years as being "extremely important" or "very important" to decision-making on a 5-point Likert scale. To plan for arteriovenous fistula referral, the respondents deemed thresholds which would predict probability of kidney failure between >30 and >50 % at 1 year, as useful, while many commented that the rate of progression should be included for decision-making. Over 80 % of the respondents were not satisfied with their current ability to predict the progression to kidney failure, CV events, and death. Most of them indicated that they would value and use validated risk scores for decision-making. CONCLUSIONS: Our national survey of nephrologists shows that the risk prediction for major adverse clinical outcomes is valuable in CKD at multiple time frames and risk thresholds. Further research is required in developing relevant and meaningful risk prediction models for clinical decision-making in patient-centered CKD care.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".