A Scoping Review of Physiotherapeutic Interventions for Trismus in Head and Neck Cancer: Where Is the Manual Therapy?
Bibliographic record
Abstract
Purpose: Trismus, or restricted mouth opening, is a common side effect of treatment for head and neck cancer. This scoping review examined the characteristics, extent, and nature of existing research on manual therapy and jaw-mobilizing devices to prevent and manage trismus related to head and neck cancer. Method: Six electronic databases were searched using the terms trismus, head and neck cancer, and physical therapy and the associated MeSH terms. The review focused on the factors related to intervention delivery: timing, adherence, completion rates, and adverse events. Results: Nine studies were included. Eight examined the use of a jaw-mobilizing device, and one explored the benefit of remote telephone support. Two studies involved cancer survivors at risk of trismus, five involved survivors with trismus, and two included survivors both with and at risk of trismus. No studies were found examining physiotherapist provision of manual therapy. Within-group comparisons supported the benefit of using a jaw-mobilizing device to manage trismus, whereas significant between-groups differences were found only in non-randomized controlled trials. Survivor symptoms and intervention burden were reported reasons for poor adherence and completion rates. Conclusions: No benefit was found for the use of jaw-mobilizing devices for the prevention of trismus. Given the potential of manual therapy to enhance outcomes, physical therapist–led research is warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".