Economical Evaluation of Cancer Types Using Intensity-Modulated Radiation Therapy Compared to 3D Conformal Radiation Therapy: A Systematic Review
Bibliographic record
Abstract
Background: Cancer is the second most common cause of death worldwide. Economic evaluation of cancer treatment to reduce costs can save the health care system millions of dollars while optimizing care. Therefore, this systematic review aimed to study the economic evaluation of cancer treatment using intermediate intensity radiation therapy (IMRT) compared to conventional 3D conformal radiation therapy (3D-CRT). Methods: Literatures from PubMed, Embase, Cochran Library, Google scholar, Scopus and Iranian databases were retrieved since Jan 2000 to Apr 2020 for eligible English studies. The quality of the studies was evaluated using Cheers' checklist and then the textual data were analyzed manually by content analysis method. Results: Overall, 1790 articles were retrieved, of which 12 studies were reviewed. The article quality score ranged from 14.5 to 23 out of a maximum of 24 points. Eleven studies referred to cost-effectiveness analysis and one study referred to cost-utility analysis. Studies have been conducted in the United States, Canada, Australia, Brazil, the Netherlands, the United Kingdom, and Hungary. IMRT appears to be a cost-effective treatment strategy for rectal cancer, soft tissue sarcoma, and localized carcinoma of the pharynx, and for prostate cancer in terms of prolonging survival, but it is a cost-effective treatment strategy for head cancer. In addition, the neck was not in India's cancer control program. Conclusion: The results can help to decide whether to use radiation therapy and radiotherapy in the standard treatment path. Furthermore, they underline that IMRT treatment technique was cost effective for a long-time care service.
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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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".