Cost-Effectiveness of Positron Emission Tomography in Recurrent Colorectal Cancer in Canada
Bibliographic record
Abstract
Several studies over the past decade have demonstrated that 2-fluoro-2-D-[18F] fluorodeoxyglucose (FDG) positron emission tomography (PET) is more accurate than computed tomography (CT) for the staging of recurrent colorectal carcinoma. This study uses quantitative decision tree modeling and sensitivity analysis to assess the cost-effectiveness of a PET-based management strategy for staging recurrent colorectal carcinoma in Canada. Both management costs and life expectancy are determined. METHODS: Two patient management strategies were compared - one using CT alone and one using both CT and PET. A survey of recent literature was used to construct a meta-analyses of available studies for the accuracy of PET in staging recurrent colorectal carcinoma. Life expectancies were determined from recent Canadian statistics, and expected life expectancies with disease were calculated from published survival rates. Management costs were determined from: estimates of the installation cost of PET facilities in Canada; management costs from our institutions; and recently published Canadian cost estimates of various procedures. RESULTS: A cost savings of $1,758 per person is expected for a PET and CT strategy, along with a slight increase in life expectancy (3.8 days), when compared with a CT alone strategy. This cost savings stemmed from avoided surgeries and remained in favour of the PET strategy when subjected to a rigorous sensitivity analysis.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".