Chemotherapy increases the prevalence of radiotherapy-related trismus in head and neck cancer patients: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: To evaluate the influence of chemotherapy on the prevalence of trismus in irradiated head and neck cancer patients.Material and Methods: This systematic review guided by PRISMA-2020 and registered in PROSPERO (CRD42021255377) screened 963 articles in 7 scientific-databases (PubMed, Lilacs, Livivo, Scopus, Embase, Web of Science, EBSCO) and 3 grey-literature databases (Open Grey, Google Scholar, ProQuest) and eight articles were included for qualitative synthesis, meta-analysis (combined odds ratio, inverse variance method plus random effects), heterogeneity analysis (I² and Tau²), one-of-out evaluation and publication bias analysis (Eggs' and Begg's tests) (RevMan®, p<0.05).The Newcastle-Ottawa Quality Assessment Scale Cohort Studies was used to assess the risk of bias (RoB).The classification assessment, development, and recommendations (GRADE) approach was used to assess the certainty of evidence.Results: The eight articles evaluated 1474 patients treated with chemoradiotherapy and 858 patients treated with radiotherapy.Five articles had low RoB, and three had high RoB.Chemoradiotherapy significantly (p=0.0003)increased the prevalence of trismus (OR=2.55,95% CI = 1.53-4.23)compared to radiotherapy, with significant (p=0.010)but low heterogeneity (I²=59%;Tau²=0.29).There was no significant risk of publication bias, one-out analysis showed no significant difference between studies, and GRADE showed a moderate level of evidence.Trismus was directly associated to worse quality of life.Conclusions: The incidence of trismus increases when chemotherapy is combined with radiotherapy for head and neck cancer, which negatively impacts the quality of life.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.022 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".