The Power of Placebo to Restore Neurological Function After Spinal Cord Injury: Implications for Neuromodulation
Bibliographic record
Abstract
BACKGROUND: Emerging trials demonstrate that neuromodulation, especially spinal cord stimulation, improves function for those with chronic spinal cord injury. Their design - uncontrolled and unblinded - is justified by the claim that sham conditions are unethical and/or impossible. In the absence of controlled trials, the functional benefits of spinal cord stimulation cannot be distinguished from the effects of placebo. OBJECTIVES: To discuss the validity of the claim that placebo control conditions are infeasible in spinal cord stimulation research, and to propose feasible solutions for including sham conditions that would account for placebo effects. RESULTS: Placebo effects are likely to occur in spinal cord stimulation studies, given the high levels of participant expectations of an effect, natural fluctuations in symptoms associated with spinal cord injury, regression towards the mean, the Hawthorne effect, presence of concurrent interventions, and the absence of blinding in existing studies. Options for placebo control conditions could include adding an "untreated" control group, using "placebo-resistant" outcomes, adding an active comparator group or sham stimulation, or investing in parasthesia-free stimulation. Additionally, wherever feasible, blinding of both participants and assessors should be pursued. CONCLUSIONS: The current evidence base for spinal cord stimulation is undermined by the lack of rigorous sham controls, and the argument that such controls are unethical or unfeasible do not withstand scrutiny. We propose strategies for the inclusion of placebo controls in future trials and encourage investigators to prioritize these approaches to ensure the true benefit of spinal cord stimulation can be determined.
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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 | 0.231 | 0.452 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".