Chronic Kidney Disease of Unknown Etiology (CKDu) – The Search for Causes and the Impact of Its Politicization
Bibliographic record
Abstract
Kidney disease of unknown etiology (CKDu) has been identified in many countries extending from Mesoamerica and Egypt to South-east Asia and China. Although CKDu has been associated by various authors to farming, the association is an artifact of treating multimodal distributions as unimodal. There is NO correlation of CKDu with agriculture since affected farming villages are often surrounded by other farming villages free of CKDu. Initial studies looked for a correlation of CKDu with the presence of toxic heavy metal residues of arsenic, cadmium, etc., or herbicides like glyphosate as causative factors. There is now some consensus that their concentrations are below danger thresholds, be it in Mesoamerica or south-east Asia. The conceptual basis of a search for etiology within a systems approach is discussed, and names proposed for the disease that bias the identification of its etiology are reviewed. Current research has narrowed down the etiology to geochemical electrolytic contaminants like fluorides and ionic components in hard water, nanosilica, as well as renal toxins similar to indoxyl sulfates that may arise from interactions of ions with humic acids contained in aqueous organic matter. However, while agrochemical toxins are increasingly considered less relevant to the etiology of CKDu, it has become a firm public belief. In Sri Lanka, this has spawned ideology-based agricultural policies leading to partial and complete banning of agrochemicals followed by some back tracking, strongly disrupting the economy and the food supply.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".