Hyaluronate carboxymethylcellulose sheets for the prevention of adhesive complications: a model‐based cost–utility analysis
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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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".