Reusable Surgical Gowns Yield Annual Health Care Economic Benefits- The Assessment of Annual Costs of Reusable Versus Disposable Surgical Gowns
Bibliographic record
Abstract
Reusable surgical gowns are important in hospitals and when compared to disposable surgical gowns offer significant economic benefits. This study was initiated to quantify the annual cost saving to hospitals by selecting the reusable surgical gown option by using data from 127 separate hospitals over the period of January to December, 2021. These laundries are all separate organizations with their own collection and processing methods, ownership, scale, and varying ages of equipment. All are in competitive markets and so it is assumed to be representative of the larger domain of U.S and Canadian laundries serving hospitals. Annual cost savings were calculated as the difference in annual disposable and reusable costs divided by the annual disposable cost (as a percent). For a representative hospital system there is nearly a 50% annual cost savings which accrues to the health care organization’s bottom line. Said differently, selecting disposable surgical gowns increases the hospitals surgical gown budget by about 190%. For the entire U.S. health care system (6,129 hospitals), a shift to 90% reusable surgical gowns would yield a health care savings of about $354 million per year, a beneficial step.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".