Bibliographic record
Abstract
Movement disorders often emerge from the interplay of complex pathophysiological processes involving the kidneys and the nervous system. Tremor, myoclonus, ataxia, chorea, and parkinsonism can occur in the context of renal dysfunction (azotemia and electrolyte abnormalities) or they can be part of complications of its management (dialysis and renal transplantation). On the other hand, myoglobinuria from rhabdomyolysis in status dystonicus and certain drugs used in the management of movement disorders can cause nephrotoxicity. Distinct from these well-recognized associations, it is important to appreciate that there are several inherited and acquired disorders in which movement abnormalities do not occur as a consequence of renal dysfunction or vice versa but are manifestations of common pathophysiological processes affecting the nervous system and the kidneys. These disorders are the emphasis of this review. Increasing awareness of these conditions among neurologists may help them to identify renal involvement earlier, take timely intervention by anticipating complications and focus on therapies targeting common mechanisms in addition to symptomatic management of movement disorders. Recognition of renal impairment in a patient with complex neurological presentation may narrow down the differentials and aid in reaching a definite diagnosis.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".