Half of synthetic conduits are discarded in open cardiac and vascular surgeries
Bibliographic record
Abstract
Objective This report aims to quantify discarded vascular graft material during cardiovascular surgeries and evaluate the associated financial implications. Understanding graft waste can inform strategies for reducing healthcare costs and improving resource efficiency. Methods Discarded synthetic vascular conduit remnants were collected from 36 open cardiac and vascular surgeries at the McGill University Health Centre (MUHC) between 2022 and 2024. Procedure type, graft specifications, and residual graft lengths were recorded. Leftover graft lengths were measured using a standardized digital technique (ImageJ). Waste was calculated as a percentage of the original graft length. A cost analysis was performed based on procurement data, with national projections extrapolated from institutional data. Cost of discarded material (price ÷ labeled length) does not imply linear pricing across items nor full life-cycle costs. Results On average per graft, 22.1±11.5 cm of graft material, or 50.0±20.8% of original length, was discarded. Polyester grafts exhibited higher waste (54.5±19.6%) compared to expanded polytetrafluoroethylene (ePTFE) grafts (39.4±19.5%, p=0.03). No significant difference was found between aortic (18.5±13.8 cm) and bypass (24.2±9.10 cm) procedure per-graft waste (p=0.17). This may cost our center nearly CAD 196,000 yearly, and nationally, this translates to kilometers of wasted graft material. Conclusions Nearly half of vascular graft material remains unused, underscoring significant inefficiencies in graft utilisation. Standardizing graft lengths, optimizing procurement strategies, and exploring customizable packaging may substantially reduce material waste and healthcare costs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".