Bibliographic record
Abstract
There has been renewed interest in the surgical treatment of Parkinson's disease (PD) over the past 20 years. In the 1940's to 1960's many PD patients underwent neurosurgical procedures to ablate specific brain targets to alleviate tremor and, to a lesser extent, akinesia and rigidity. With the introduction of levodopa in the 1960s, and the realization of its striking benefits, surgical treatment of movement disorders virtually disappeared. With time, limitations and adverse effects associated with drug treatment became all too apparent. With complications associated with long-term drug treatment, particularly levodopa-induced motor fluctuations and dyskinesias, limiting therapeutic effectiveness in many patients, surgery has been reexamined to address this unmet need. This has lead to the development and, now, widespread adoption of high-frequency deep brain stimulation (DBS). DBS has been shown to be a safe and effective treatment for dopaminergic motor symptoms of PD, particularly tremor, rigidity, and bradykinesia, and has resulted in important reductions in motor complications of medical therapy. While DBS provides important symptomatic benefit, it does not appear to alter the natural history of PD. Other surgical strategies, including cellular transplantation and gene therapy aiming at neural repair and restoration, are currently being examined, but these have yet to be proven useful.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".