Eat All Through Radiation Therapy (EAT‐RT): Structured therapy model to facilitate continued oral intake through head and neck radiotherapy—User acceptance and content validation
Bibliographic record
Abstract
BACKGROUND: To develop and examine user acceptance and content validity of a structured program to facilitate safe but challenging oral intake during radiotherapy (RT) delivered by a speech language pathologist (SLP)-the Eat-All Through Radiation Therapy (EAT-RT) program. METHODS: EAT-RT was developed through expert consensus of SLPs at the Princess Margaret Cancer Centre (Canada) and M D Anderson Cancer Center using a conceptual framework of a diet hierarchy and a mealtime routine. EAT-RT was refined by practicing SLPs, and then disseminated for a 4-week clinical pilot at seven sites who were subsequently invited to participate in an online survey. RESULTS: Twelve SLPs from six sites piloted EAT-RT therapy with a median of eight patients (IQR: 2-15) before and/or during RT. All SLPs reported EAT-RT added value to their practice, harmonized well with exercises, and its content was helpful; 11 (92%) reported EAT-RT facilitated patient understanding and indicated the desire to continue using EAT-RT. CONCLUSION: The EAT-RT program was accepted by North American SLPs. The findings support the content and value of EAT-RT to facilitate oral intake in patients with head and neck cancer throughout RT.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".