The Effectiveness of Surgical Methods for Trismus Release at Least 6 Months After Head and Neck Cancer Treatment: Systematic Review
Bibliographic record
Abstract
Background The objective of this systematic review was to identify the different surgical treatment modalities of severe trismus after head and neck squamous cell cancer treatment. Methods An electronic literature database search was conducted in Medline, Embase, Cochrane, Web of Science, and OpenGrey to determine articles published up to September 2021. Two observers independently assessed the identified papers for eligibility according to PRISMA guidelines. The inclusion criteria were trismus after head and neck squamous cell cancer with consecutive treatment, detailed description of the surgical procedure for trismus release, description of the initial treatment, at least 6 months between initial cancer treatment and trismus release surgery, a minimal follow-up (FU) of 6 months, and availability of full text. The quality was evaluated using the Newcastle-Ottawa scale. A subanalysis of the maximal mouth opening (MMO) was performed using a mixed-effect model. Results A total of 8,607 unique articles were screened for eligibility, 69 full texts were reviewed, and 3 studies, with a total of 46 cases, were selected based on the predetermined inclusion and exclusion criteria. Three treatment strategies were identified for trismus release (1) free flap reconstruction (FFR), (2) coronoidectomy (CN), and (3) myotomy (MT). There was a clear improvement for all treatment modalities. A quantitative analysis showed a beneficial effect of CN (mean 24.02 ± 15.02 mm) in comparison with FFR (mean 19.88 ± 13.97 mm) and MT (mean 18.38 ± 13.22 mm) (P < 0.01*). An increased gain in MMO after trismus release was found if no primary resection was performed (P = 0.014*). Two studies included in the analysis had an intermediate risk of bias and one had a low risk of bias. Conclusion Currently available reports suggest a low threshold for performing a CN compared with FFR and MT. There is a need for high-quality randomized controlled trials with carefully selected and standardized outcome measures.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".