Challenges in reporting surgical site infections to the national surgical site infection surveillance and suggestions for improvement
Bibliographic record
Abstract
INTRODUCTION: Mandatory orthopaedic surgical site infection (SSI) data in England are used as a benchmark to compare infection rates between participating hospitals. According to the national guidelines, trusts are required to submit their data for at least one quarter of the year but they are free to report for all quarters. Owing to this ambiguity, there is a concern about robust reporting across trusts and therefore the accuracy of these data. There is also concern about the accuracy of collection methods. The aim of this five-year retrospective study was to assess the accuracy of SSI reporting at two hospitals in South East England under the same trust. METHODS: A retrospective review was carried out of five years of electronic medical records, microbiology data and readmission data of all patients who underwent hip and knee replacement surgery at these hospitals. These data were validated with the data submitted to Public Health England (PHE) and any discrepancy between the two was noted. RESULTS: A significant difference was found in the SSI rates reported by the surveillance staff and our retrospective method. CONCLUSIONS: Our study confirms the findings of a national survey, which raised concerns about the quality of SSI reporting and the usefulness of PHE SSI data for benchmarking purposes. To our knowledge, there are no previously published studies that have looked at the accuracy of the English orthopaedic SSI surveillance. In the light of our findings, there is an urgent need for external validation studies to identify the extent of the problem in the surveillance scheme. The governing bodies should also issue clear guidelines for reporting SSIs to maintain homogeneity and to present the true incidence of SSI. We suggest some measures that we have instituted to address these inadequacies that have led to significant improvements in reporting at our trust.
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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 | 0.301 | 0.556 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.015 | 0.010 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".