International Survey of Nephrologists about Referral of Patients with Advanced CKD for Kidney Transplantation
Bibliographic record
Abstract
Background: Kidney transplantation (KTx) is the treatment of choice for patients with kidney failure and could be lifesaving in countries with limited access to dialysis. However, studies have shown low rates of referrals from high-income countries (HIC);17-33% within the first year of chronic dialysis initiation, while data is lacking in low-income countries (LIC). Methods: An ISN-TTS working group created a Knowledge, Attitude and Practice (KAP) survey, sent to nephrologists globally via the ISN mailing list. Responses are collected anonymously and sorted per respondents’ countries income level. HIC and middle-high were combined into HIC and LIC and middle-low were combined into LIC. The survey is currently being administered. We report preliminary analysis of the first 100 responses to 9 compiled questions across KAP pillars. Results: Respondents from 55 countries are 62% males, 60% 30-50 years, and 85% work at academic centers. Living and/or deceased donor KTx is available in 92.5% and HIC comprise 52%. Knowledge questions were answered similarly in both cohorts, except for referral of elderly patients (P=0.01), patients with nonadherence (P=0.014), or financial hurdles (P=0.027). Responses to attitude questions are shown in Fig 1. Based on participants’ practice, patients in LIC vs. HIC were referred as follows: elderly <15% vs. 45% (P<0.0001), preemptively 70% vs. 90% (P<0.004), combined Tx 8% vs. 30% (P=0.0002); patients with cancer in remission 25% vs. 55% (P=0.007); financial difficulties 22% vs. 65% (P<0.0001). Conclusion: Our results highlight KTx referral pattern differences between LIC and HIC. Educational activities should address practitioners’ needs and optimize KTx referral for patients worldwide.Case scenario questions, responses are based on comfort level (Likert scale:1 uncomfortable–5 very comfortable)
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".