Impact of Wait times on Spinal Cord Stimulation Therapy Outcomes
Bibliographic record
Abstract
OBJECTIVE: Presently, the long-term success rate of spinal cord stimulation (SCS) ranges from 47% to 74%. SCS efficacy is inversely proportional to the passage of time between development of chronic pain syndrome and time of implantation. To improve outcomes, implantation should be performed early. This study identifies sources of delay and offers suggestions for improvement. METHODS: A retrospective analysis of 437 SCS patients examines delays to accessing SCS at various points in the referral stream, from initial diagnosis, family physician, and various specialist treatments, to implantation. Analysis of variance evaluated the effect of age, sex, treating specialty, and their interactions on implantation delay. A multiple linear regression model was developed to assess factors contributing to implantation delay. RESULTS: From time of onset of chronic pain to implantation, patients endured a delay of 65.4 ± 2.04 months. Initial physician contact occurred at a mean of 3.4 ± 0.12 months after development of pain syndrome. Family physicians managed cases for 11.9 ± 0.45 months and various specialists for an additional 39.8 ± 1.22 months. Neurosurgeons were quickest to refer to an implant physician (average wait-time 32.28 ± 2.64 months), while orthopedic surgeons and nonimplanting anesthesiologists took the longest, contributing to wait times of 51.60 ± 5.04 months and 58.08 ± 5.76 months, respectively. Once the decision for implantation was made, the implanting physician required 3.31 ± 0.09 months to organize the procedure. A gradual decline in wait times was observed from 1980 to present. CONCLUSION: To improve SCS success rates, physicians involved in the treatment for chronic pain should refer these cases early to an implant physician once failure of medical management becomes apparent.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".