Cost-effectiveness of positron emission tomography/computed tomography in the management of advanced head and neck cancer.
Bibliographic record
Abstract
BACKGROUND: Positron emission tomography/computed tomography (PET/CT) is a newer imaging modality that combines whole-body PET using [(18)F]fluorodeoxyglucose (FDG) and contrasted high-resolution CT. This has the advantage of combining the functional imaging of FDG-PET with the anatomic detail afforded by CT. OBJECTIVE: To assess the cost-benefit of whole-body PET/CT as a diagnostic tool in head and neck cancer. METHODS: A retrospective cohort (American Joint Committee on Cancer III-IVB squamous cell carcinoma in 2003) was reviewed for costs of diagnostic tests, distant metastases, and treatment type. This was compared to a hypothetical cohort of patients using PET/CT as a sole diagnostic tool using the current literature on test characteristics in the detection of distant metastases. The main outcome measure was the cost of the diagnostic workup, and the secondary outcome measure was the cost of the treatment. RESULTS: The cost of the traditional workup was $450 per patient, whereas the cost of PET/CT workup was $722 per patient. The sensitivity of the traditional workup for lung metastases at 12 months was 14.3%. The average cost of curative surgery was $81,290, radiotherapy was $8,224, and chemotherapy was $1,158. In the cohort of 76 patients reviewed, improved PET/CT sensitivity would theoretically detect three more cases of metastatic disease and reduce the total cohort cost of treatment by $198,526 by relegating these patients to palliation. CONCLUSIONS: PET/CT is a more expensive test when used alone in the diagnostic workup of head and neck cancer but results in an overall cost savings by reducing the number of futile radical treatments. There is a cost benefit to the use of PET/CT as the diagnostic and staging test for head and neck cancer patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".