A Qualitative Study of Factors Influencing the Decision to Have an Elective Amputation
Bibliographic record
Abstract
BACKGROUND: Some patients with a functionally impaired lower limb choose to have an elective amputation, whereas others do not. Functional outcomes do not favor either type of treatment, making this a complex decision. The experiences of patients who have chosen elective amputation were analyzed to identify the key factors in this decision-making process. METHODS: Patients from a tertiary care amputee clinic who had chosen to undergo elective amputation of a functionally impaired lower limb participated in the present study. A qualitative research design involved the use of one-on-one semi-structured interviews, which were audio recorded and transcribed. Narrative analysis was used by three researchers to provide triangulation. Recurrent key themes and patterns were described. Personal factors in the decision-making process were identified. RESULTS: Factors that had the largest impact on the decision-making process were pain, function, and participation. Body image, self identity, and the opinions of others had little influence. Satisfaction with the surgical outcome was related to how closely the result matched the patient's expectations. Patients who were better informed prior to surgery had more realistic expectations about living with an amputation. CONCLUSIONS: The severity of pain and the desire for improved function are strong drivers for patients deciding to undergo elective amputation of a functionally impaired lower extremity. While patients do not want others' opinions, information regarding life with an amputation helps to set realistic expectations regarding outcome.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".