Improving wound infection management: education and evaluation of an infection management pathway
Bibliographic record
Abstract
OBJECTIVE: To assess the clinical impact and acceptance of an infection management (IM) pathway, designed to improve the consistency of care of wound infection when introduced, and supported by an educational programme. METHOD: An education and evaluation programme (T3 programme) was-conducted in Portugal, Spain and Italy. This consisted of a two-hour educational, virtual seminar, followed by a four-week evaluation of an IM pathway during which survey data were collected on the impact of this pathway on clinician-selected patients. Finally, all participants reconvened for a virtual meeting during which the combined results were disseminated. The pathway provided guidance to clinicians regarding the targeted use of antimicrobial wound dressings according to the presence and absence of signs and symptoms of wound infection. RESULTS: Responses relating to 259 patients treated according to the IM pathway, 139 (53.7%) of whom had received previous antimicrobial treatment, were captured. Signs and symptoms of infection resolved within four weeks of treatment in >90% of patients. All 25 patients who had received prior antimicrobial treatment for ≥3 months experienced a resolution in the signs and symptoms of infection within four weeks. The majority of participating clinicians agreed that the IM pathway improved decision-making (94.9%) and confidence (97.3%), and helped to determine the correct antimicrobial treatment (91.4%) in the context of wound infection. CONCLUSION: The T3 programme was an efficient way to deliver a structured educational programme. The use of the IM pathway resulted in >90% of patients achieving resolution of their signs and symptoms of wound infection.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".