Application of restorative neurostimulation for chronic mechanical low back pain in an older population with 2-year follow up
Bibliographic record
Abstract
Introduction Data on the Medicare-aged population show that older patients are major consumers of low back pain (LBP) interventions. An effective approach for patients with mechanical LBP that has been refractory to conservative management is restorative neurostimulation. The efficacy of restorative neurostimulation has been demonstrated in multiple prospective studies, with published follow-up over 4 years, showing a consistent durable effect. Methods To further examine the effect of restorative neurostimulation in an older demographic, data from three clinical studies were aggregated: ReActiv8-B prospectively followed 204 patients, ReActiv8-C study prospectively followed 87 patients and ReActiv8-PMCF prospectively followed 42 patients. Two hundred and sixty-one patients were identified with complete 2-year follow-up and divided into cohorts of equal size based of age quartiles. At 2 years from device activation, patients in either cohort were classified by change in disability (Oswestry Disability Index (ODI)) or change in pain score(NRS/VAS) and assessed as proportion of patients per group at each time point. Additionally, health-related quality of life (HRQoL) (EQ5D-5L) was longitudinally compared with baseline. Differences in proportions were assessed using χ2 and continuous variables by repeated measures analysis of variance. Results The oldest quartile (n=65) had a median age of 60 (56–82) years compared with the entire population (n=261) who had a median age of 49 (22–82) years. The completer analysis on patients with 2 years of continuous data showed improvement of a 50% in pain was achieved by 62% and 65% and a 15-point ODI improvement in 48% and 60% in the oldest quartile and entire population, respectively. HRQoL (EuroQol 5-Dimension) improved from baselines of 0.568 and 0.544 to 0.763 and 0.769 in the oldest quartile and entire population respectively. All age quartiles improved statistically and clinically over baseline. Conclusions This aggregate analysis of three independent studies provides insight into the performance of restorative neurostimulation in an older population. Patients derived significant and clinically meaningful benefit in disability, pain and HRQoL. When compared with a similarly indicated cohort of younger patients, there were no statistically or clinically significant differences.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".