Parkinson disease primer, part 2: management of motor and nonmotor symptoms
Bibliographic record
Abstract
OBJECTIVE: To provide family physicians with an approach to the management of motor and nonmotor symptoms of Parkinson disease (PD). SOURCES OF INFORMATION: Published guidelines on the management of PD were reviewed. Database searches were conducted to retrieve relevant research articles published between 2011 and 2021. Evidence levels ranged from I to III. MAIN MESSAGE: Family physicians can play an important role in identifying and treating motor and nonmotor symptoms of PD. Family physicians should initiate levodopa treatment for motor symptoms if they affect function and if specialist wait times are long, and they should be aware of basic titration approaches and possible side effects of dopaminergic therapies. Abrupt withdrawal of dopaminergic agents should be avoided. Nonmotor symptoms are common and underrecognized and are a major factor in disability, quality of life, and risk of hospitalization and poor outcomes for patients. Family physicians can manage common autonomic symptoms such as orthostatic hypotension and constipation. Family physicians can treat common neuropsychiatric symptoms such as depression and sleep disorders, and they can help recognize and treat psychosis and PD dementia. Referrals to physiotherapy, occupational therapy, speech language therapy, and exercise groups are recommended to help preserve function. CONCLUSION: Patients with PD present with complex combinations of motor and nonmotor symptoms. Family physicians should have basic knowledge of dopaminergic treatments and their side effects. Family physicians can play important roles in management of motor symptoms and particularly nonmotor symptoms and can have a positive impact on patients' quality of life. An interdisciplinary approach involving specialty clinics and allied health experts is an important part of management.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".