Application of Manual Therapy for Dysphagia in Head and Neck Cancer Patients: A Preliminary National Survey of Treatment Trends and Adverse Events
Bibliographic record
Abstract
BACKGROUND: Radiation-associated dysphagia is a common and debilitating consequence of treatment for head and neck cancer (HNC). Since commonly employed dysphagia therapy programs for HNC patients still lack authoritative efficacy, some speech-language pathologists (SLPs) have started employing manual therapy (MT) techniques in an attempt to prevent or rehabilitate dysphagia in this patient population. However, exceptionally little is known about the use of MT in this patient population. OBJECTIVES: The purpose of this study was to describe practice patterns as well as the rate, type, and severity of adverse events associated with SLP provision of MT to HNC patients. METHODS: An Internet-based questionnaire geared toward SLPs who practice MT was developed and sent to SLPs practicing in the United States, 3 times, through 3 national listservs (American Speech Language Hearing Association [ASHA] Special Interest Division 13, ASHA Special Interest Division 3, and University of Iowa Voiceserv), over the course of 4 weeks. RESULTS: Of the 255 respondents, 116 (45.5%) performed MT on HNC patients. Of these 116 SLPs, 27.6% provided proactive MT during radiation, 62.1% provided 1 to 2 sessions per week, and 94.8% prescribed a MT home program. The rate, type, and severity of reported adverse events were similar between HNC and non-HNC patients. CONCLUSION: This preliminary survey demonstrated that SLPs provide MT to HNC patients during and after cancer treatment, and that reported adverse events paralleled those experienced by noncancer patients. However, these results should be taken with caution, and a well-designed prospective study is needed to formally establish the safety and the preliminary efficacy of this novel clinical intervention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".