Economic outcomes associated with deep surgical site infection from lower limb fractures following major trauma
Bibliographic record
Abstract
Aims This study aims to estimate economic outcomes associated with 30-day deep surgical site infection (SSI) from closed surgical wounds in patients with lower limb fractures following major trauma. Methods Data from the Wound Healing in Surgery for Trauma (WHiST) trial, which collected outcomes from 1,547 adult participants using self-completed questionnaires over a six-month period following major trauma, was used as the basis of this empirical investigation. Associations between deep SSI and NHS and personal social services (PSS) costs (£, 2017 to 2018 prices), and between deep SSI and quality-adjusted life years (QALYs), were estimated using descriptive and multivariable analyses. Sensitivity analyses assessed the impact of uncertainty surrounding components of the economic analyses. Results Compared to participants without deep SSI, those with deep SSI had higher mean adjusted total NHS and PSS costs (adjusted mean difference £1,577 (95% confidence interval (CI) -951 to 4,105); p = 0.222), and lower mean adjusted QALYs (adjusted mean difference -0.015 (95% CI -0.032 to 0.002); p = 0.092) over six months post-injury, but this difference was not statistically significant. The results were robust to the sensitivity analyses performed. Conclusion This study found worse economic outcomes during the first six months post-injury in participants who experience deep SSI following orthopaedic surgery for major trauma to the lower limb. However, the increase in cost associated with deep SSI was less than previously reported in the orthopaedic trauma literature. Cite this article: Bone Jt Open 2022;3(5):398–403.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".