Improving wound swab collection in paediatric patients: a quality improvement project
Bibliographic record
Abstract
Microbiology sample swabs may be unsuccessful or rejected for a variety of reasons. Typically, errors occur in the preanalytical phase of sample collection. Errors with collection, handling and transport can lead to the need to repeat specimen collection. Unsuccessful specimens contribute to delays in diagnosis, increased patient stress and increased healthcare costs. An audit of sample swabs from London Health Sciences Centre Children’s Hospital from August through October 2021 yielded complete success rates of 100% for ear and eye culture swabs, 98.1% for methicillin-resistant Staphylococcus aureus swabs and 88.9% for wound swabs. This project aimed to improve wound swab success to 95% on the paediatric inpatient and paediatric emergency departments by May 2022. Stakeholders from paediatric clinical services including physicians, nurses and the laboratory medicine team at our centre were engaged to guide quality improvement interventions to improve specimen success rate. Based on feedback, we implemented visual aids to our electronic laboratory test information guide. Additionally, visual reminders of correct sample collection equipment were placed in high traffic areas for nursing staff. After the interventions were implemented, a three-month follow-up showed that wound swab success rate rose to 95.3%. This study achieved its aim of improving wound swab success rate to 95%. It adds to the growing pool of evidence that preanalytical phase intervention such as visual aids can increase swab success rates, in healthcare settings.
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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 | 0.048 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".