The New Zealand Surgical Site Infection Improvement (SSII) Programme: a national quality improvement programme reducing orthopaedic surgical site infections.
Bibliographic record
Abstract
AIMS: The New Zealand Surgical Site Infection Improvement (SSII) Programme was established in 2013 to reduce the incidence of surgical site infections (SSI) in publicly funded hip and knee arthroplasties in New Zealand hospitals. METHODS: The programme pursued a three-pronged strategy: 1. Surveillance of SSI with a nationwide system 2. Promotion of consistent adherence to evidence-based practices proven to reduce SSI 3. Monitoring and publicly reporting changed practice and outcome data. RESULTS: Between quarter 3 2013 and quarter 4 2016 there has been a nationwide increase in compliance with all process measures: correct timing for antibiotic prophylaxis; use of the recommended antibiotic in the recommended dose and alcohol-based skin antisepsis. The SSI rate in hip and knee arthroplasties has shown a significant improvement. The nationwide median rate has fallen to 0.91% since June 2015, compared with 1.36% during the baseline period of April 2013 to March 2014 (p<0.01). This equates to approximately 55 fewer infections between August 2015 and June 2017, savings of NZD$2.2 million in avoided treatment and avoided disability-adjusted life years (DALYs) of NZD$5 million. CONCLUSIONS: The introduction of a nationwide SSI reduction programme for hip and knee arthroplasties resulted in an increase in compliance across the country with best practice that was associated with a reduction in incidence of SSI since June 2015 from the baseline period of April 2013 to March 2014, sustained to June 2017.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".