Non-invasive brain neuromodulation techniques for phantom limb pain: a systematic review.
Bibliographic record
Abstract
eview question / Objective P: Participants with phantom limb pain; I: non-invasive brain neuromodulation techniques (NIBS); C: Control group received sham treatment or placebo; O: Pain intensity (any pain scale reported in the trial, such as visual analogue scale); S: RCT.The aim is to evaluate the effects of NIBS in the treatment of phantom limb pain to evaluate the effects of non-invasive brain neuromodulation techniques for phantom limb pain.Condition being studied About 50% -80% of amputees will experience PLP.Phantom limb pain (PLP) is characterized as the painful sensation experienced in the missing limb after amputation.PLP is a serious public health problem that can affect the physical, psychological, and functional health of amputees.The current treatment options (pharmacological and behavioral) for PLP are not entirely effective.NIBS is a promising treatment technology and previous studies have found its potential in treating neuropathic pain. METHODS Participant or population Participants with phantom limb pain.Intervention Non-invasive brain neuromodulation techniques such as repetitive transcranial magnetic stimulation, transcranial direct current stimulation.Comparator Sham treatment or placebo. Study designs to be included Randomized controlled trial.Eligibility criteria Our inclusion criteria were as follows: i) studies that evaluated any beneficial or adverse effect of the use of noninvasive neuromodulation techniques in the treatment of adults (>18 years old) with a PLP diagnosis.ii) randomized controlled trials (RCTs), including parallel-group and crossover designs, and quasiexperimental (QE) studies; iii) studies with 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".