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 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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".