Efficacy of interferential current on relieving pain of musculoskeletal origin – protocol of a systematic review and meta-analysis undertaken
Bibliographic record
Abstract
Introduction and Objective.Interferential current IFC is one of the common electrotherapeutic modalities used in the treatment of painful conditions.Patients with musculoskeletal pain seek medical help in order to reduce their pain that could be achieved using IFC.The current review aims to analyze the recently available information regarding the efficacy of the IFC in alleviating the pain of musculoskeletal origin.State of knowledge.IFC, as one of the medium frequency currents, has the advantage of being more comfortable and deeply penetrating so that it can reach deeper painful tissues.It has been proposed that IFC can relieve pain through stimulating different body mechanisms, such as the gate mechanism and the release of body opioids.However, the evidence behind the effectiveness of IFC as a pain-relieving modality for musculoskeletal pain has been poorly studied and still not conclusive.Conclusions.This systematic review will summarize the effects of IFC on relieving musculoskeletal pain as reported through improvement in visual analog scale, numeric pain rating scale, or the McGill pain questionnaire.Through searching multiple databases and including randomized controlled trials published during the last ten years, the findings of the current systematic review and meta-analysis will establish the quality of the recently available evidence and demonstrate if there will be a need for further studies.
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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 | 0.029 | 0.036 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.018 | 0.026 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 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; 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".