Checklist on the Quality of the Repetitive Peripheral Magnetic Stimulation (rPMS) Methods in Research: An International Delphi Study
Bibliographic record
Abstract
An increasing number of clinical research studies have used repetitive peripheral magnetic stimulation (rPMS) in recent years to alleviate pain or improve motor function. rPMS is non-invasive, painless, and administrated over peripheral nerve, spinal cord roots, or a muscle using a coil affixed to the skin and connected to a rapid-rate magnetic stimulator. Despite the clinical impact and scientific interest, the methodological inconsistencies or incomplete details and findings between studies could make the rPMS demonstration difficult to replicate. Given the lack of guidelines in rPMS literature, the present study aimed at developing a checklist to improve the quality of rPMS methods in research. An international panel of experts identified among those who had previously published on the topic were enrolled in a two-round web-based Delphi study with the aim of reaching a consensus on the items that should be reported or controlled in any rPMS study. The consensual rPMS checklist obtained comprises 8 subject-related items (e.g., age, sex), 16 methodological items (e.g., coil type, pulse duration), and 11 stimulation protocol items (e.g., paradigm of stimulation, number of pulses). This checklist will contribute to new interventional or exploratory rPMS research to guide researchers or clinicians on the methods to use to test and publish rPMS after-effects. Overall, the checklist will guide the peer-review process on the quality of rPMS methods reported in a publication. Given the dynamic nature of a consensus between international experts, it is expected that future research will affine the checklist.
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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 | 0.273 | 0.316 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".