Anaesthesia techniques and advanced monitoring in CANVAS patients - Implications for postoperative morbidity and patient recovery: A case report
Bibliographic record
Abstract
INTRODUCTION: Cerebellar ataxia, neuropathy and vestibular areflexia syndrome (CANVAS) is a rare multisystem neurodegenerative disorder. We describe our perioperative evaluation and care of a patient with CANVAS undergoing a pancreaticoduodenectomy for an ampullary adenocarcinoma, with a focus on perioperative risk stratification and optimisation, intraoperative advanced haemodynamic monitoring and the postoperative care. CASE PRESENTATION: A 69-year-old female with CANVAS presented with asymptomatic obstructive jaundice, icterus and abdominal pain. She had limited mobility and deconditioning due to severe generalised neuropathy. Computed tomography confirmed a resectable periampullary tumour. Her Duke Activity Status Index was 8.25 points and Edmonton Frailty Scale score was 11, confirming moderate frailty. However, the Charlson Comorbidity Index was five, indicative of a 21% estimated 10-year survival. Further risk stratification including respiratory function testing, echocardiography and cardiopulmonary exercise testing was conducted. The patient proceeded with surgery after multidisciplinary discussions with her treating medical teams. DISCUSSION: CANVAS is a rare and challenging condition requiring careful perioperative planning and management. There is no effective treatment for CANVAS. The management approach focuses on mitigating symptoms and improving quality of life. Given that no specific guidelines for managing these patients in the perioperative period have been provided, this report highlights several critical medical issues and implications that should be considered for the successful management of these patients. We demonstrate the role of specific anaesthesia techniques and advanced haemodynamic monitoring in both preventing postoperative morbidity and optimising patient recovery. CONCLUSION: CANVAS is a rare and challenging condition in anaesthesia requiring careful perioperative planning and management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".