Clinical application of low-level laser therapy (Photo-biomodulation therapy) in the management of breast cancer-related lymphedema: a systematic review
Bibliographic record
Abstract
BACKGROUND: Breast cancer-related lymphedema (BCRL) is a frequent issue that arises after mastectomy surgery in women and compromises physical and mental function. Previously published studies have shown positive effects with the use of Low-level laser therapy in another term Photo-biomodulation therapy (PBM). This research investigated the efficacy of clinical use of LLLT (PBM) in the treatment of metastatic breast cancer-related lymphedema. METHODS: PubMed, PEDro, Medline, and the Cochrane Library were searched for LLLT clinical trials published before October 2021. The methodological quality of randomized trials and the effectiveness of Laser Therapy for BCRL were evaluated. The primary objectives were arm circumference or arm volume, whereas the secondary goals were to assess shoulder mobility and pain severity. RESULTS: Eight clinical trials were analyzed in total. Typically, the included RCTs had good research quality. At four weeks, there was a considerable reduction in arm circumference/volume, and this continued with long-term follow-up. However, no statistically significant change in shoulder mobility or pain severity was seen between the laser and placebo groups at 0-, 1-, 2-, and 3-month short-term follow-up. CONCLUSIONS: The findings of this comprehensive study demonstrated that LLLT (PBM) was successful in diminishing arm circumference and volume than improving shoulder mobility and pain. Data indicates that laser therapy (PBM) may be a beneficial treatment option for females with PML. Because of the scarcity of evidence, there is a strong need for well-conducted and longer-duration trials in this field. TRIAL REGISTRATION: Details of the protocol for this systematic review were registered on PROSPERO and can be accessed at www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42022315076 .
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.005 | 0.007 |
| 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".