Prehabilitation in head and neck cancer patients: A literature review
Bibliographic record
Abstract
BACKGROUND: Dysphagia is one consequence of head and neck cancer that has a significant impact on quality of life for head and neck cancer survivors. While survival rates continue to improve, focus has shifted to maximizing long-term function, with prevention or prehabilitation programs becoming more common. Prehabilitation programs typically include an exercise regime that specifies the exercise type, the number of repetitions to complete per set, the number of sets of each exercise to complete per day, as well as the length of the treatment block. Ideally, exercise programs are designed with principles of neuromuscular plasticity in mind. METHODS: Twenty-nine original research articles published between 2006 and 2020 were included in this state-of-the-art review and examined for program timing and details. RESULTS: Two definitions for prehabilitation were noted: one third of the studies defined prehabilitation as preventative exercises prior to the start of acute cancer treatment; the remaining two thirds defined prehabilitation as treatment concurrent prehabilitation. Exercises prescribed ranged from general stretching and range of motion exercises, to trismus and swallowing specific exercises. The most common swallowing specific exercise was the Mendelsohn's maneuver, followed by the effortful swallow, Shaker, and Masako maneuver. The most common dose was 10 repetitions of an exercise, three times per day for the duration of radiation therapy. The most common measures were questionnaires, followed by g-tube dependence, mouth opening, and MBS reports. CONCLUSION: This review of the literature has shed light on the variability of prehabilitation timing, exercise type, dose, duration of treatment, and outcomes associated with prehabilitation, making the selection of an optimal prehabilitation program difficult at this time.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".