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Record W134508750

Transoral laser microsurgery versus radiation therapy for early glottic cancer in Canada: cost analysis.

2009· article· en· W134508750 on OpenAlexaffabout
Timothy J. Phillips, Chady Sader, Timothy S. Brown, Martin Bullock, Derek Wilke, Jonathan Trites, Rob Hart, Michael Murphy, S. Mark Taylor

Bibliographic record

VenuePubMed · 2009
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTransoral laser microsurgeryMedicineRadiation therapyMicrosurgeryQuality of life (healthcare)CancerNova scotiaMedical physicsSurgeryHead and neck cancerInternal medicineHistory
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score0.661

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.284
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations44
Published2009
Admission routes2
Has abstractyes

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