Hyaluronate carboxymethylcellulose sheets for the prevention of adhesive complications: a model‐based cost–utility analysis
Why this work is in the frame
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Bibliographic record
Abstract
AIM: Clinical trials suggest that hyaluronate carboxymethylcellulose (HA/CMC) prevents adhesion-related complications after intra-abdominal surgery, but at a high upfront cost. This study evaluated the cost-effectiveness of HA/CMC for patients undergoing curative-intent open colorectal cancer surgery. METHODS: Using a Markov Monte Carlo microsimulation model, we conducted a cost-utility analysis comparing the cost-effectiveness of HA/CMC at curative-intent open colorectal cancer surgery versus standard management. We considered a scenario where HA/CMC was used at the index operation only, as well as where it was used at the index operation and any subsequent operations. The perspective was that of the third-party payer. Costs and utilities were discounted 1.5% annually, with a 1-month cycle length and 5-year time horizon. Model input data were obtained from a literature review. Outcomes included cost, quality-adjusted life-years (QALYs), small bowel obstructions (SBOs) and operations for SBO. RESULTS: Using HA/CMC at the index operation results in an incremental cost increase of CA$316 and provides 0.001 additional QALYs, for an incremental cost-effectiveness ratio of CA$310,000 per QALY compared to standard management. In our simulated cohort of 10,000 patients, HA/CMC prevented 460 SBOs and 293 surgeries for SBO. Probabilistic sensitivity analysis found that HA/CMC was cost-effective in 18.5% of iterations, at a cost-effectiveness threshold of CA$50,000 per QALY. Results of the scenario analysis where HA/CMC was used at the index operation and any subsequent operations were similar. CONCLUSIONS: Hyaluronate carboxymethylcellulose prevents adhesive bowel obstruction after open colorectal cancer surgery but is unlikely to be cost-effective given minimal long-term impact on healthcare costs and QALYs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 it