Bibliographic record
Abstract
PURPOSE OF REVIEW: Deep brain stimulation (DBS) is a well accepted treatment modality for many movement disorders such as Parkinson's disease and an increasing number of other functional neurological disorders like dystonias and epilepsy. This review will highlight the recent developments in our knowledge regarding the effects of anesthetic agents on neurophysiologic recording and anesthetic management of patients undergoing the insertion of a DBS. RECENT FINDINGS: There are new indications for DBS as well as new therapeutic target nuclei that are being examined. Better surgical technique and new imaging techniques like frameless stereotaxy are likely to improve patient tolerance of these procedures. The effects of anesthetic drugs on nuclei microelectrode recording and the need for an awake and cooperative patient for intraoperative macrostimulation testing continue to be the challenge for the anesthesiologist. Intracranial hemorrhage, seizures, and venous air embolism are the important perioperative complications needing urgent care. There are reports of increased incidence of postoperative behavioral and cognitive problems after DBS insertion. SUMMARY: There will continue to be an increase in the use of DBS for many neurological and functional disorders, especially in the aging baby boomer population. Anesthetic technique will vary depending on the prevalent practice in individual institutions and requirements of the specific surgical procedure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.016 |
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".