The changing landscape of surgery for Parkinson's Disease
Bibliographic record
Abstract
Neurosurgical interventions have been used to treat PD for over a century. We examined the changing landscape of surgery for PD to appraise the value of various procedures in the context of advances in our understanding and technology. We assessed the number of articles published on neurosurgical procedures for PD over time as an albeit imprecise surrogate for their usage level. We identified over 8,000 publications associated with PD surgery. Over half the publications were on DBS. The field of DBS for PD showed a rapid rise in articles, but is now in a steady state. Thalamotomy and, to a lesser extent, pallidotomy follow a biphasic publication distribution with peaks approximately 30 years apart. Articles on gene therapy and transplantation experienced initial rapid rises and significant recent declines. Procedures using novel technologies, including gamma knife and focused ultrasound, are emerging, but are yet to have significant impact as measured by publication numbers. Pallidotomy and thalamotomy are prominent examples of procedures that were popular, declined, and re-emerged and redeclined. Transplantation and gene therapy have never broken into clinical practice. DBS overtook all procedures as the dominant surgical intervention and drove widespread use of surgery for PD. Notwithstanding, the number of DBS articles appears to have plateaued. As advances continue, emerging treatments may compete with DBS in the future. © 2017 International Parkinson and Movement Disorder Society.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".