Pre-Operative Assessment of Patients Undergoing Spinal Cord Stimulation for Refractory Angina Pectoris
Bibliographic record
Abstract
Spinal cord stimulation (SCS) is used to treat a variety of chronic pain conditions refractory to more conservative management including refractory angina pectoris. We identified 31 patients who underwent SCS implantation for the indication of refractory angina at a single institution from 2003 through 2018. Sixteen patients were male, and 15 were female. Average age was 53.9 years. Prior to SCS implantation, all patients had at least one coronary angiogram. Ten (32.3%) patients had undergone percutaneous coronary intervention, and four (12.9%) had undergone coronary artery bypass grafting. Thirty patients (96.7%) were currently using anti-angina medications. Twenty-six patients (83.9%) were on antiplatelet or anticoagulant agents at the time of SCS evaluation. Spinal cord stimulation implanters must perform a comprehensive evaluation incorporating appropriate multidisciplinary care particularly in patients with refractory angina given their cardiovascular comorbidities. It is important to have baseline data (e.g., pain scores, nitroglycerin consumption, frequency of angina episodes, and a questionnaire, such as the Seattle Angina Questionnaire) to compare with follow-up data to help define treatment success. We report a single institution's pre-operative experience for patients undergoing SCS for refractory angina to illustrate unique pre-operative SCS considerations in this chronic pain population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".