An economic evaluation of palliation of dysphagia in esophageal cancer: Analysis of the TROG 03.01/NCIC ES.2 phase III study in advanced esophageal cancer in patients treated with radiotherapy versus chemoradiotherapy.
Bibliographic record
Abstract
340 Background: In advanced oesophageal cancer (OC), 90% of patients have dysphagia as a principal symptom. The randomised TROG 03.01 trial reported no significant overall survival or dysphagia relief difference between 15 fractions (#) radiotherapy alone (RT) or RT plus chemotherapy (CRT) Future studies may consider RT hypofractionation, different chemotherapy, esophageal stenting, and best supportive care. Comparing costs and outcomes, economic evaluation often informs public funding decisions in countries such as Australia and the UK. The objective of this analysis was to derive baseline cost and outcome for further studies. Methods: Given equal outcomes (non-inferiority) between treatment arms, cost-minimisation analysis (CMA) was used to evaluate cost-effectiveness, cost a deciding factor if no other benefit was predicted. We explored 15 and 10 # courses used in TROG 03.01 and alternatives, 1 or 5 # Study design and uncertainty analyses were provided. Sub-analysis assessed salvage therapy for local and systemic progression. The EQ-5D (and SF-12) was used to determine utility values to estimate Quality-Adjusted Life years (QALYs) for evaluation. Results: From a health system perspective, costs and outcomes of RT were estimated at $4,700 AUD (15 # course). If, compared with RT alone, an alternative option is more costly but more effective, then a cost-utility analysis (CUA) is preferred economic evaluation. Intervention and in/outpatient costs, initial phase and post treatment were considered for stent insertion and alternative chemotherapy regimens. Alternatively, autocontoured CT planning and machine learning were modelled to reduce RT planning cost (est. $650/p). Total cost for 5 # is $3200 could be further reduced through process efficiency savings using CMA approach. Conclusions: The value of palliative approaches in common malignancies is difficult to assess. This study uses economic analysis to guide paying authority decisions. RT can be simplified from the TROG 03.01 approach to lessen cost and simplify treatment. The clinical efficiency of 1 or 5 # should be evaluated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.010 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".