A Pragmatic Approach to the Perioperative Management of Parkinson’s Disease
Bibliographic record
Abstract
Patients with Parkinson's disease (PD) may undergo several elective and emergency surgeries. Motor fluctuations, the presence of a wide range of non-motor symptoms (NMS), and the use of several medications, often not limited to dopaminergic agents, make the perioperative management of PD challenging. However, the literature on perioperative management of PD is sparse. In this descriptive review article, we comprehensively discuss the issues in the pre-, intra-, and postoperative phases which may negatively affect the PD patients and discuss the approach to their prevention and management. The major preoperative challenges include accurate medication reconciliation and administration of the dopaminergic medications during the nil per os (NPO) state. While the former can be addressed with staff education and PD-specific admission protocols, knowledge of non-oral formulations of dopaminergic agents (apomorphine, inhalational levodopa, and rotigotine transdermal patch) is the key to the management of the Parkinsonian symptoms in NPO state. Deep brain stimulation (DBS) devices should be turned off to avert potential electromagnetic interference with surgical appliances. Choosing the appropriate anesthesia and avoiding and managing respiratory issues and dysautonomia are the major intraoperative challenges. Timely reinitiation of dopaminergic medications, adequate management of pain, nausea, and vomiting, and prevention of postoperative infections and delirium are the postoperative challenges. Overall, a multidisciplinary approach is pivotal to prevent and manage the perioperative complications in PD. Administration of anti-Parkinson medications during NPO state, prevention of anesthesia-related complications, and timely rehabilitation remain the key to healthy surgical outcomes.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".