Orthopaedic surgical site infection surveillance in NHS England
Bibliographic record
Abstract
AIMS: The importance of accurate identification and reporting of surgical site infection (SSI) is well recognised but poorly defined. Public Health England (PHE) mandated collection of orthopaedic SSI data in 2004. Data submission is required in one of four categories (hip prosthesis, knee prosthesis, repair of neck of femur, reduction of long bone fracture) for one quarter per year. Trusts are encouraged to carry out post-discharge surveillance but this is not mandatory. Recent papers in the orthopaedic literature have highlighted the importance of SSI surveillance and the heterogeneity of surveillance methods. However, details of current orthopaedic SSI surveillance practice has not been described or quantified. PATIENTS AND METHODS: All 147 NHS trusts in England were audited using a structured questionnaire. Data was collected in the following categories: data collection; data submission to PHE; definitions used; resource constraints; post-discharge surveillance and SSI rates in the four PHE categories. The response rate was 87.7%. RESULTS: Variation in practice was clear in all categories in terms of methods and timings of data collection and data submission. There was little agreement on SSI definitions. At least six different definitions were used, some trusts using more than one definition. Post-discharge surveillance was carried out by 62% of respondents but there was again variation in both the methods and staff used. More than half of the respondents felt that SSI surveillance in their unit was limited by resource constraints. SSI rates ranged from 0% to 10%. CONCLUSION: This paper quantifies the heterogeneity of SSI surveillance in England. It highlights the importance of adequate resourcing and the unreliability of relying on voluntary data collection and submission. Conformity of definitions and methods are recommended to enable meaningful SSI data to be collated. Cite this article: Bone Joint J 2017;99-B:171-4.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".