Complications of spinal cord stimulation, suggestions to improve outcome, and financial impact
Bibliographic record
Abstract
OBJECT: The long-term success of spinal cord stimulation is impeded by the high incidence of adverse events. The cost of complications to the healthcare budget is influenced by the time course needed to reverse the effect, and by the type of corrective measures required. Understanding the mechanism of complications and reducing them can improve the overall success rate and the cost factor. METHODS: The authors performed a retrospective analysis of data obtained in 160 patients treated during a 10-year period. For each category of complication, the level of healthcare resource use was assessed for each case and a unit cost was applied. The total cost of each complication was determined by summing across healthcare resource headings. All cost calculations were performed in Canadian dollars at 2005 prices. To understand the mechanics of various hardware-related complications and how to avoid them, the authors have utilized the results of bench tests conducted at Medtronic, Inc. Fifty-one adverse events occurred in 42 of the 160 patients. The complications were classified as either hardware related (39 events) or biological (12 events). The mean cost of complications during the 10-year study period was dollar 7092 (range dollar 130 - dollar 22,406). CONCLUSIONS: Complications not only disrupt the effect of pain control but also pose an added expense to the already high cost of therapy. It is possible to reduce the complication rate, and thus improve the long-term success rate, by following the suggestions made in this paper, which are supported by the biomechanics of the human body and the implanted material.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".