Is Improved Survival in Early-Stage Pancreatic Cancer Worth the Extra Cost at High-Volume Centers?
Bibliographic record
Abstract
BACKGROUND: Volume of operative cases may be an important factor associated with improved survival for early-stage pancreatic cancer. Most high-volume pancreatic centers are also academic institutions, which have been associated with additional healthcare costs. We hypothesized that at high-volume centers, the value of the extra survival outweighs the extra cost. STUDY DESIGN: This retrospective cohort study used data from the California Cancer Registry linked to the Office of Statewide Health Planning and Development database from January 1, 2004 through December 31, 2012. Stage I-II pancreatic cancer patients who underwent resection were included. Multivariable analyses estimated overall survival and 30-day costs at low- vs high-volume pancreatic surgery centers. The incremental cost-effectiveness ratio (ICER) and incremental net benefit (INB) were estimated, and statistical uncertainty was characterized using net benefit regression. RESULTS: Of 2,786 patients, 46.5% were treated at high-volume centers and 53.5% at low-volume centers. There was a 0.45-year (5.4 months) survival benefit (95% CI 0.21-0.69) and a $7,884 extra cost associated with receiving surgery at high-volume centers (95% CI $4,074-$11,694). The ICER was $17,529 for an additional year of survival (95% CI $7,997-$40,616). For decision-makers willing to pay more than $20,000 for an additional year of life, high-volume centers appear cost-effective. CONCLUSIONS: Although healthcare costs were greater at high-volume centers, patients undergoing pancreatic surgery at high-volume centers experienced a survival benefit (5.4 months). The extra cost of $17,529 per additional year is quite modest for improved survival and is economically attractive by many oncology standards.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".