Conservative Kidney Management in the Middle East and North Africa: Attitudes, Practices, and Implementation Barriers
Bibliographic record
Abstract
Introduction: Conservative kidney management (CKM) is poorly developed and not easily accessible globally, especially in middle- and low-income countries. This study aimed to understand the perspectives of nephrologists on CKM and the barriers to its implementation in the Middle East and North Africa (MENA) region. Methods: We conducted an online survey. Nephrologists were contacted through their local nephrology societies. Responses were divided into the following 3 groups as per the country's income classification by the World Bank: high-, middle-, and low-income. Results: A total of 336 surveys were analyzed (response rate: 34.28%). The mean age of participants was 43.3 ± 9.8 years; 50% were male, 91% practiced in urban settings, and 18% were affiliated with academic centers. Of the participants, 76% were from middle-income countries. Nearly 80% of the participants were aware of CKM, and 65% accepted CKM as a treatment modality for kidney failure. However, only 20% consistently offered CKM to their patients and only 16% had a formal CKM program at their institution. Among these, 12% had a multidisciplinary team and only 6% had formal CKM training. The major perceived barriers to CKM implementation were financial and resource constraints (37.7% and 32.7%, respectively). Cultural and religious barriers constituted 18.3% and 8.6%, respectively, and were similar among the 3 income groups. Conclusion: Despite the significant awareness of CKM in the MENA region, its implementation remains poor. Key barriers include financial limitations, resource shortages, and a lack of training. Regional and national research is required to address these challenges and guide policies to improve CKM accessibility and implementation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".