Patients' experience with and perspectives on neuromodulation for pain: a systematic review of the qualitative research literature
Bibliographic record
Abstract
Chronic pain has far-reaching impacts on a person's life and on society more broadly. After failure or intolerance of conservative treatments, neuromodulation may be an option for a subgroup of patients. However, little is known about the patient experience of neuromodulation. We conducted a systematic review of published qualitative research on patient experience with neuromodulation for chronic pain. Four databases were searched: MEDLINE, EMBASE, Psych INFO, and all EMB reviews, from inception to December 4, 2019. We used narrative synthesis to identify key findings from the included studies. The data were qualitatively analyzed using a modified constant comparative analysis to identify key themes across the studies. Seven thousand five hundred forty-two unique citations were retrieved. Sixty-four abstracts were selected by the reviewers and continued to full-text review. After full-text review, 57 studies were excluded with 7 studies included in this systematic review. The included studies were of high quality. Four broad themes emerged: (1) living with chronic pain, (2) expectations, (3) managing challenges, and (4) regaining normalcy. Neuromodulation should be part of an overall pain management plan that may include the need for ongoing emotional and psychosocial support. A deeper knowledge of the patient experience with neuromodulation will assist care teams in providing meaningful support to patients. The results of this study suggest that further research is needed to support neuromodulation as an option for patients living with chronic pain.
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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 | 0.067 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".