The use of McConnell taping to correct abnormal biomechanics and muscle activation patterns in subjects with anterior knee pain: a systematic review
Bibliographic record
Abstract
[Purpose] The aim of this review was to present the available evidence for the effect of McConnell taping on knee biomechanics in individuals with anterior knee pain. [Methods] The PubMed, Medline, Cinahl, SPORTDiscus, PEDro and ScienceDirect electronic databases were searched from inception until September 2014. Experimental research on knee biomechanical or EMG outcomes of McConnell taping compared with no tape or placebo tape were included. Two reviewers completed the searches, selected the full text articles, and assessed the risk of bias of eligible studies. Authors were contacted for missing data. [Results] Eight heterogeneous studies with a total sample of 220 were included in this review. All of the studies had a moderate to low risk of bias. Pooling of data was possible for three outcomes: average knee extensor moment, average VMO/VL ratio and average VMO-VL onset timing. None of these outcomes revealed significant differences. [Conclusion] The evidence is currently insufficient to justify routine use of the McConnell taping technique in the treatment of anterior knee pain. There is a need for more evidence on the aetiological pathways of anterior knee pain, level one evidence, and studies investigating other potential mechanisms of McConnell taping.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".