Highlighting the gaps in quantifying the economic burden of surgical site infections associated with antimicrobial-resistant bacteria
Bibliographic record
Abstract
Antibiotics are the pillar of surgery from prophylaxis to treatment; any failure is potentially a leading cause for increased morbidity and mortality. Robust data on the burden of SSI especially those due to antimicrobial resistance (AMR) show variable rates between countries and geographical regions but accurate estimates of the incidence of surgical site infections (SSI) due to AMR and its related global economic impact are yet to be determined. Quantifying the burden of SSI treatment is an incentive to sensitize governments, healthcare systems, and the society to invest in quality improvement and sustainable development. However in the absence of a unified epidemiologically sound infection definition of SSI and a well-designed global surveillance system, the end result is a lack of accurate and reliable data that limits the comparability of estimates between countries and the possibility of tracking changes to inform healthcare professionals about the appropriateness of implemented infection prevention and control strategies. This review aims to highlight the reported gaps in surveillance methods, epidemiologic data, and evidence-based SSI prevention practices and in the methodologies undertaken for the evaluation of the economic burden of SSI associated with AMR bacteria. If efforts to tackle this problem are taken in isolation without a global alliance and data is still lacking generalizability and comparability, we may see the future as a race between the global research efforts for the advancement in surgery and the global alarming reports of the increased incidence of antimicrobial-resistant pathogens threatening to undermine any achievement.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".