A prospective audit on the effect of training and educational workshops on the incidence of urethral catheterization injuries
Bibliographic record
Abstract
INTRODUCTION: The incidence of iatrogenic urethral catheterization (UC) injuries is approximately 0.3%. Resultant complications are associated with patient morbidity and unnecessary healthcare costs. Our aim was to investigate whether educational training workshops decreased the incidence of UC-related injuries. METHODS: A prospective audit was performed to calculate incidence, morbidity, and costs associated with iatrogenic UC injury from January to July 2015. Educational workshops were then conducted with healthcare staff and training modules for junior doctors. UC-related incidence, morbidity, and costs in the subsequent six-month period were recorded prospectively and compared with the previous data. RESULTS: The incidence of iatrogenic UC injuries was reduced from 4.3/1000 catheters inserted to 3.8/1000 catheters after the intervention (p=0.59). Morbidity from UC increased in the second half in the form of increase in cumulative additional inpatient hospital stay (22 to 79 days; p=0.25), incidence of urosepsis (n=2 to n=4), and need for operative intervention (n=1 to n=2). The cost of managing UC injuries almost doubled in the period after the training intervention (€50 449 to €90 100). CONCLUSIONS: Current forms of educational and training interventions for UC did not significantly change morbidity or cost of iatrogenic UC injuries despite a decrease in incidence. Improved and intensive training protocols are necessary for UC to prevent avoidable iatrogenic complications, as well as a safer urethral catheter design.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".