Improving Culturally Sensitive Care in Digit Amputations: A Quality Improvement Project
Bibliographic record
Abstract
Introduction: Many patients have spiritual or cultural preferences regarding the disposal and reclamation of amputated parts, yet little is known about the current process. Our goal was to understand patient preferences and increase patient education regarding amputation disposal and reclamation. Methods: A quality improvement approach was used. Stakeholders were interviewed to process map current methods. Amputation patients were surveyed to determine preferences for amputation disposal and reclamation. Using the Plan-Do-Study-Act, change concepts were applied and outcomes measured through the same survey. Descriptive methods, analysis of variance, unpaired t -tests, and run/statistical process control charts were used for analysis. Results: Stakeholders identified barriers against amputation reclamation, including lack of patient access to resources, limited provider clarity surrounding the process, and no standardized policies. Baseline preferences were first gathered from 31 patients: 42% felt educated about the disposal process, 42% were concerned about the treatment of their amputated part, 74% wished to be more informed, and 16% wanted to reclaim their part. Change concepts were then implemented. First, a presentation was given at teaching rounds for medical trainees and staff. Results showed no significant change. Second, an educational handout about amputation disposal was distributed to patients. This showed a significant improvement in patient education, decreased concern for the management of amputated parts, and decreased needs for further discussion with health care providers. Conclusions: Many patients have preferences for amputation disposal. Patients mostly value education and awareness. The format through which education is provided is important—access to educational material may be most beneficial for patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".