Interventions for myofascial pain syndrome in cancer pain: recent advances: why, when, where and how
Bibliographic record
Abstract
PURPOSE OF REVIEW: Pain is one of the most feared and most common symptoms of cancer, experienced by 38-85% of patients. Pain in terminally ill cancer patients is a multidimensional experience caused by a diverse array of factors - cancer itself, its treatment or other causes. Studies have shown a high incidence of myofascial pain syndrome (MPS) in patients with cancer and the knowledge of myofascial trigger points (MTrPs) is important to address and manage existing pain, and to prevent the recurrence of pain. This review aims to summarize recent advances in interventions for managing MPS in patients with cancer. RECENT FINDINGS: Database searches were conducted on MEDLINE, CINAHL, and Google Scholar to locate all studies published from inception until April 2019 using the keywords cancer pain, myofascial pain, TrPs with emphasis of any methodological quality that included interventions for MPS. MPS in advanced cancer patients are more commonly observed along with other cancer pains rather than independently with a prevalence of 11.9-48% in those patients diagnosed with cancer of head and neck and breast cancer. SUMMARY: Interventional therapies employing ultrasound guided injection of the MTrPs is gaining popularity in the management of MPS in cancer pain and may be a better alternative than the use of opioid analgesics in the multidisciplinary management.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".