A Quality Improvement Initiative to Decrease the Incidence of Post Operative Wound Complications Following Minor Level Amputations
Bibliographic record
Abstract
atient education is important for improving the ability to manage one’s health and optimize clinical outcomes. We identified a paucity in educational materials for patients who undergo a partial foot amputation at our institution and developed an educational brochure to address this deficit as part of a quality improvement initiative. Our aim was to increase the proportion of patients receiving pre- operative education and improve the preparation of vascular surgery patients who will undergo partial foot amputations. We produced an educational brochure to improve patient knowledge and address any preoperative concerns. The effectiveness of this educational material was assessed with a questionnaire in a cohort of patients followed since January 2023. One Plan-Do-Study-Act (PDSA) cycle has been completed thus far. To date, five patients have completed the questionnaire. Three patients provided verbal responses. Sixty-six percent of patients found that the information in the brochure improved their knowledge and that the language of the brochure was easy to understand. Seventy-five percent of patients were confident with caring for their wound in the post-operative period. Educational materials are an important pillar of patient care, particularly in helping to prepare patients for minor amputations where there is a high failure rate and conversion to major amputations. Our brochure was effective in improving patient awareness and knowledge before surgery.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".