Impact of penicillin allergy labels on surgical site infections in a large UK cohort of gastrointestinal surgery patients
Bibliographic record
Abstract
Abstract Objectives Studies in the USA, Canada and France have reported higher surgical site infection (SSI) risk in patients with a penicillin allergy label (PAL). Here, we investigate the association between PALs and SSI in the UK, a country with distinct epidemiology of infecting pathogens and range of antimicrobial regimens in routine use. Methods Electronic health records and national SSI surveillance data were collated for a retrospective cohort of gastrointestinal surgery patients at Cambridge University Hospitals NHS Foundation Trust from 1 January 2015 to 31 December 2021. Univariable and multivariable logistic regression were used to examine the effects of PALs and the use of non-β-lactam-based prophylaxis on likelihood of SSI, 30 day post-operative mortality, 7 day post-operative acute kidney injury and 60 day post-operative infection/colonization with antimicrobial-resistant bacteria or Clostridioides difficile. Results Our data comprised 3644 patients and 4085 operations; 461 were undertaken in the presence of PALs (11.3%). SSI was detected after 435/4085 (10.7%) operations. Neither the presence of PALs, nor the use of non-β-lactam-based prophylaxis were found to be associated with SSI: adjusted OR (aOR) 0.90 (95% CI 0.65–1.25) and 1.20 (0.88–1.62), respectively. PALs were independently associated with increased odds of newly identified MRSA infection/colonization in the 60 days after surgery: aOR 2.71 (95% CI 1.13–6.49). Negative association was observed for newly identified infection/colonization with third-generation cephalosporin-resistant Gram-negative bacteria: aOR 0.38 (95% CI 0.16–0.89). Conclusions No evidence was found for an association between PALs and the likelihood of SSI in this large UK cohort, suggesting significant international variation in the impact of PALs on surgical patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".