Diagnosis of manganism and manganese neurotoxicity: A workshop report
Bibliographic record
Abstract
With declining exposures to manganese (Mn) in occupational settings, there is a need for more sensitive exposure assessments and clinical diagnostic criteria for manganism and Mn neurotoxicity. To address this issue, a workshop was held on November 12-13, 2020, with international experts on Mn toxicity. The workshop discussions focused on the history of the diagnostic criteria for manganism, including those developed by the Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail (IRSST) in Quebec in 2005 and criteria developed by the Chinese government in 2002 and updated in 2006; the utility of biomarkers of exposure; recent developments in magnetic resonance imaging (MRI) for assessing Mn accumulation in the brain and diagnosing manganism; and potential future applications of metabolomics. The suggestions of the participants for updating manganism diagnostic criteria included the consideration of: i) A history of previous occupational and environmental exposure to Mn; ii) relevant clinical symptoms such as dystonia; iii) MRI imaging to document Mn accumulation in the neural tissues, including the basal ganglia; and iv) criteria for the differential diagnosis of manganism and other neurological conditions. Important research gaps include the characterization of Mn exposure and other co-exposures, exploration of the roles of different brain regions with MRI, understanding the complexity of metal ion transporters involved in Mn homeostasis, and a need for information on other neurotransmitter systems and brain regions underlying the pathophysiology of manganism.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".