Nephrotoxic Mushroom Poisoning: Global Epidemiology, Clinical Manifestations, and Management
Bibliographic record
Abstract
Because mushroom poisonings are increasing worldwide after ingestions of known, newly described, and formerly considered edible species, the objectives of this review are to describe the global epidemiology of nephrotoxic mushroom poisonings, to identify nephrotoxic mushrooms, to present a toxidromic approach to earlier diagnoses of nephrotoxic mushroom poisonings based on the onset of acute renal failure, and to compare the outcomes of renal replacement management strategies. Internet search engines were queried with the keywords to identify scientific articles on nephrotoxic mushroom poisonings and their management during the period of 1957 to the present. Although hepatotoxic, amatoxin-containing mushrooms cause most mushroom poisonings and fatalities, nephrotoxic mushrooms, most commonly Cortinarius species, can cause acute renal insufficiency and failure. Several new species of nephrotoxic mushrooms have been identified, including Amanita proxima and Tricholoma equestre in Europe and Amanita smithiana in the United States and Canada. In addition, the edible, hallucinogenic mushroom Psilocybe cubensis has been noted recently via mass spectrometry as a rare cause of acute renal insufficiency. Renal replacement therapies including hemodialysis are often indicated in the management of nephrotoxic mushroom poisonings, with renal transplantation reserved for extracorporeal treatment failures.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".