The physical experiences of women with lower limb absence during pregnancy and postpartum: symptoms, prosthesis management & mobility
Bibliographic record
Abstract
PURPOSE: Little information is available to women with lower limb absence (LLA) and their health care providers regarding the impacts they may experience during the perinatal period. This study explores the physical impacts of pregnancy on women with LLA, including mobility, prosthesis fit and prosthesis use. METHODS: We conducted semi-structured interviews with 19 women with LLA who had experienced pregnancy in the last 10 years. Interviews were analyzed using thematic analysis. RESULTS: Substantial variation exists in the experience of women's physical symptoms, prosthesis management and mobility. Physical symptoms were similar to any pregnant individual, but the impacts were more substantial. As volume change in the residual limb can impact prosthesis fit, self-management techniques and prosthetist adjustments were used to manage it. Pregnancy impacted the way in which women were mobile and the activities they chose to participate in. A wide variety of creative mobility solutions were utilized to complete activities including prosthesis use, assistive equipment and adaptive movement. CONCLUSIONS: Women with LLA and their health care providers must be aware of the wide range of experiences women face during pregnancy and treat each pregnancy uniquely. Planning ahead and working with a health care team can mitigate many of these challenges.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".