Pregnancy after amputation: A national survey of prosthetic and mobility outcomes in women with lived experience
Bibliographic record
Abstract
BACKGROUND: In pregnancy, women with limb loss or deficiency or lower extremity amputations (LEAs) may experience physiologic changes affecting mobility, prosthesis use, and/or pain. However, little is known about the pregnancy-related experiences of these women. OBJECTIVE: The objective of this research was to characterize pregnancy-related experiences including complications, impact on prosthesis use, gait aid use, and mobility for women with LEAs. STUDY DESIGN: A national, self-administered online survey. Data were analyzed using descriptive statistics. Thematic analyses were performed on open-ended questions. Participants were women with 1 or more LEA(s) who had been pregnant within the last 5 years and were recruited via social media, LEA clinics, and word of mouth. RESULTS: Sixteen women from 4 Canadian provinces completed the survey describing 31 pregnancies. A total of 9 had acquired LEAs and 7 had congenital LEAs. All but 1 respondent had unilateral LEAs; most common level was transfemoral (38%). All wore a prosthesis daily and were K-level 4 ambulators. Five (31%) had to decrease or stop prosthesis use, and 6 (25%) required a gait aid or wheelchair during pregnancy. The most common pregnancy-associated complications were low back pain (64%), changes to limb size/prosthesis comfort (64%), reduced balance (44%), falls (38%), and postpartum depression (25%). CONCLUSION: This survey is the first to describe the many unique challenges women with LEAs may experience in pregnancy and highlights important information for women with LEAs, their health care providers, their rehabilitation team, and avenues for future research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".