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Record W1929606760 · doi:10.1002/jso.23974

Making a case for high‐volume robotic surgery centers: A cost‐effectiveness analysis of transoral robotic surgery

2015· article· en· W1929606760 on OpenAlexaff
Luke Rudmik, Wenyi An, Devon Livingstone, Wayne Matthews, Hadi Seikaly, Rufus Scrimger, Deborah A. Marshall

Bibliographic record

VenueJournal of Surgical Oncology · 2015
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsInstitute of Health Services and Policy ResearchAlberta Health ServicesUniversity of Alberta HospitalUniversity of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMedicineTransoral robotic surgeryNeck dissectionIncremental cost-effectiveness ratioCost effectivenessStage (stratigraphy)SurgeryQuality-adjusted life yearCarcinomaInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the cost-effectiveness of transoral robotic surgery (TORS) compared to intensity-modulated radiotherapy (IMRT) for early stage (T1-2, N0, M0) oropharyngeal squamous cell carcinoma (OPSCC). PATIENTS AND METHODS: A Markov decision tree model with a 5-year time horizon was developed. Comparative groups were: i) TORS with concurrent ipsilateral neck dissection +/- adjunctive IMRT, and ii) primary IMRT. Primary outcome was cost/quality adjusted life year (QALY). Perspective was the United States third party payer. Costs and effects were discounted at a rate of 3.5%. A threshold and probabilistic sensitivity analysis were performed. RESULTS: TORS strategy cost $30,992 and provided 4.81 QALYs/patient. The IMRT strategy cost $26,033 and provided a total of 4.78 QALYs/patient. The incremental cost effectiveness ratio for TORS vs. IMRT in the reference case was $165,300/QALY. The probability that TORS is cost-effective compared to IMRT at a maximum willingness-to-pay threshold of $50,000/QALY is 42%. CONCLUSION: An IMRT strategy for management of early stage OPSCC is more likely to be cost-effective compared to TORS. To improve the value of TORS for early stage OPSCC, consolidating TORS procedures to create high-volume centers of excellence may be a potential strategy to increase incremental effectiveness and reduce incremental costs. J. Surg. Oncol. 2015 111:155-163. © 2015 Wiley Periodicals, Inc.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
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 head0.154
GPT teacher head0.402
Teacher spread0.248 · 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

Citations46
Published2015
Admission routes1
Has abstractyes

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