Manganese and Movement Disorders: A Review
Bibliographic record
Abstract
Scientific and technological advances achieved with industrial expansion have led to an ever-increasing demand for heavy metals. This demand has, in turn, led to increased contamination of soil, water and air with these metals. Chronic exposure to metals may be detrimental not only to occupational workers but also to the nonoccupational population exposed to these metals. Manganese (Mn), a commonly used heavy metal, is an essential cofactor for many enzymatic processes that drive biological functions. However, it is also a potential source of neurotoxicity, particularly in the field of movement disorders. The typical manifestation of Mn overexposure is parkinsonism, which may be difficult to differentiate from the more common idiopathic Parkinson's disease. In addition to environmental exposure to Mn, other potential etiologies causing hypermanganesemia include systemic health conditions, total parenteral nutrition and genetic mutations causing Mn dyshomeostasis. In this review, we critically analyze Mn and discuss its sources of exposure, pathophysiology and clinical manifestations. We have highlighted the global public health impact of Mn and emphasize that movement disorder specialists should record a detailed social and occupational history to ensure that a toxic etiology is not misdiagnosed as a neurodegenerative disease. In the absence of a definite therapeutic option, early diagnosis and timely institution of preventive measures are the keys to managing its toxic effects.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".