Exploring the use of rehabilitation in individuals with head and neck cancer undergoing treatment: a scoping review
Bibliographic record
Abstract
PURPOSE: Explore the use, characteristics, feasibility, and functional outcomes of rehabilitation interventions used for individuals with head and neck cancer (HNC) during treatment. METHODS: Searches were conducted in four databases from Jan 2011 to Dec 31, 2022. Included studies had to include adults with HNC undergoing treatment, a rehabilitation intervention, an assessment of functional outcome(s) addressed by the International Classification of Functioning Framework (ICF) and be published in English language. Title and abstract screening, full-text review, and data extraction were completed independently, in duplicate. Descriptive statistics and a qualitative synthesis summarized findings. RESULTS: Twenty-seven studies were included in this review. The majority of studies were randomized controlled trials (70%). Most individuals represented in the included studies were males (92% of all participants) between 50 and 60 years of age. Interventions led by a speech language pathologist (33%) were most commonly described. Sixteen studies (59%) described primary outcomes that fit the ICF "impairment" domain. CONCLUSIONS: We identified few studies that explored the use, feasibility, and effectiveness of rehabilitation interventions for individuals with HNC during treatment. Future research should assess the effectiveness of rehabilitation interventions on functional outcomes beyond the ICF body function and structure domain.
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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 | 0.015 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".