PET-CT compared to no PET-CT in the management of potentially resectable colorectal cancer liver metastases: The costs implications of a randomized controlled trial.
Bibliographic record
Abstract
296 Background: PETCAM was a randomized trial evaluating the effect of PET-CT compared to conventional imaging (control) on the surgical management of patients with resectable colorectal cancer liver metastases (CRLM). It concluded that PET-CT did not result in frequent change in surgical management (8·0%, 21/263) with only 2·7% (7/263) avoidance of liver resections. In this study we conducted a cost analysis of these two arms up to one year following randomization. Methods: Health care utilization was collected for all study participants. Unit costs for hospitalization, physician services, chemotherapy and outpatient radiological and endoscopic procedures were obtained from administrative databases. Cost analysis was undertaken from the perspective of a third-party payer (i.e., Ministry of Health). Mean cost with its 95% credible interval was estimated using a Bayesian approach. Results: The estimated mean cost per patient in the PET-CT arm was CAN $45,454 (min-max: 1,340-181,420) and in the control arm, CAN $40,859 (min-max: 279-293,558), with a net difference of CAN $4,327, 95% credible interval -2,207 to 10,614. The primary cost driver was cost of hospitalization for liver surgery (+ $2,997 CAN for the PET-CT arm), mainly due to a longer length of hospital stay for the PET-CT arm compared to control (median 7 days vs. 6 days, P= 0·034) and a higher rate of postoperative complications (52/255, 20% vs. 13/128, 10%, P = 0·014). Baseline characteristics were similar between groups, including a similar number of liver segments involved with cancer, number of segments resected and type of liver resection performed. Conclusions: PET-CT does not appear to provide a significant clinical benefit in the surgical management of patients with resectable CRLM and it is not cost saving compared to control.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".