Evidence-based Advances in the Treatment of Motor Features of Parkinson's Disease
Bibliographic record
Abstract
SUMMARY Evidence-based medicine evaluates evidence and synthesizes information to succinct conclusions and recommendations. Despite this, there are gaps for clinicians as many clinically relevant questions have not been examined. Guidelines with clear criteria for evaluating evidence (from the American Academy of Neurology, NICE and the Movement Disorders Society) are contrasted and important clinical issues that are not currently addressed will be highlighted. Through weighing both motor benefit and complications of therapy, clinicians may use levodopa for patients with a low risk of dyskinesia, or a higher risk of confusion, delirium or impulse control disorders, or pre-existing daytime sleepiness. For patients with higher risk of dyskinesia and a low risk of confusion, delirium and impulse control disorders, and without pre-existing daytime sleepiness, dopamine agonists may be the dopaminergic drug of choice. Evidence for treating motor complications of illness does not provide hierarchical choice. Evidence for surgical treatments is limited by practical issues of blinding surgery. Longer term complications of surgery need to be balanced with potential benefits of surgical procedures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".