Defining minimal clinically important differences in pain and disability outcomes of patients with chronic pain treated with spinal cord stimulation
Bibliographic record
Abstract
OBJECTIVE: Minimal clinically important difference (MCID) thresholds for a limited number of outcome metrics were previously defined for patients with failed back surgery syndrome (FBSS) at 6 months after spinal cord stimulation (SCS). This study aimed to further define MCID values for pain and disability outcomes. Additionally, the authors established 1-year MCID values for outcome measures with previously defined metrics commonly used to assess SCS efficacy. METHODS: Preoperative and 1-year postoperative outcomes were collected from 114 patients who received SCS therapy for FBSS, complex regional pain syndrome, and neuropathic pain. MCID values were established for the numerical rating scale (NRS), Oswestry Disability Index (ODI), Beck Depression Inventory (BDI), McGill Pain Questionnaire (MPQ), and Pain Catastrophizing Scale (PCS). Four established anchor-based methods were utilized to compute MCID values with two anchored questions: "Are you satisfied with SCS therapy?" and "Would you have SCS surgery again?" For each question, patients were categorized as responders if they answered "yes" or as nonresponders if they responded "no." The methodologies utilized to compute MCID scores included the average change method, minimum detectable change approach, change difference calculation, and receiver operating characteristic (ROC) analysis. Area under the ROC curve (AUC) analysis has been shown to inform the accuracy at which the MCID value can distinguish responders from nonresponders and was analyzed for each instrument. RESULTS: For the first time, ranges of MCID values after SCS were established for MPQ (1-2.3) and PCS (1.9-13.6). One-year MCID values were defined for all indications: NRS (range 0.9-2.7), ODI (3.5-6.9), and BDI (2-5.9). AUC values were significant for NRS (0.78, p < 0.001), ODI (0.71, p = 0.003), MPQ (0.74, p < 0.001), and PCS (0.77, p < 0.001), indicating notable accuracy for distinguishing satisfied patients. CONCLUSIONS: This was the first study to successfully determine MCID values for two prominent instruments, MPQ and PCS, used to assess pain after SCS surgery. Additionally, previously established MCID values for ODI, BDI, and the visual analog scale for patients with FBSS at 6 months after treatment were explored at 12 months for the most common indications for SCS. These data may better inform physicians of patient response to and success with SCS therapy.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".