Transoral laser microsurgery versus radiation therapy for early glottic cancer in Canada: cost analysis.
Bibliographic record
Abstract
OBJECTIVE: There is debate over whether radiation therapy or transoral laser microsurgery (TLM) is the superior treatment for early glottic cancer. Both offer similar cure rates and posttherapy quality of life. One factor that could decide the optimum therapy is cost. Several studies in Europe and the United States show that TLM is the most cost-effective treatment. The goal of this study was to conduct the first cost analysis in Canada on the treatment of early glottic cancer comparing radiation therapy and TLM. DESIGN AND METHODS: The study was conducted retrospectively. TLM and radiation therapy were broken down into individual components, and then the price for each component was summed. SETTING: The study was conducted at the Queen Elizabeth II Health Science Centre in Halifax, Nova Scotia. MAIN OUTCOME MEASURES: The main outcome measure was total cost. RESULTS: It was found that radiation therapy was approximately four times more expensive than TLM. CONCLUSIONS: This study suggests that TLM should be the preferred treatment option for treating early glottic cancer in Canada as it is the most economical and has been shown in previous studies to be as effective as radiation therapy in both cure rates and quality of life.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".