Anesthetic Management of a Patient With Spasmodic Torticollis Treated With a Deep Brain Stimulator Implant Operated for Inguinal Hernia
Bibliographic record
Abstract
Spasmodic torticollis (ST) is the most frequent and familiar type of focal dystonia. Nowadays, deep brain stimulation (DBS) seems to be an effective treatment for ST and is considered a first-line therapy. Anesthesia in such patients requires special considerations, because of the characteristics of the disease as well as the respective treatment. We present the anesthetic management of a patient with ST treated with DBS. A 53-year-old man with idiopathic cervical dystonia treated with a deep brain stimulator device underwent an inguinal hernia correction under general anesthesia. A detailed preanesthesia evaluation was performed to assess any issues associated with the disease especially concerning the airway. A total intravenous anesthesia technique was planned. The device was turned off immediately after induction and turned on before emergency. The patient did not report any discomfort and the stimulator did not present any malfunction or interference with other devices. Anesthetic management of patients with ST treated with deep brain stimulator should focus on a careful preanesthesia evaluation regarding primarily the airway examination, a detailed planning of the anesthetic technique to be applied, and a correct handling of the DBS implanted as it can interfere with other monitoring and therapeutic devices, sometimes with severe consequences. J Med Cases. 2016;7(9):376-378 doi: http://dx.doi.org/10.14740/jmc2588w
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".