Sex Differences in Spatiotemporal Gait Parameters of Transtibial Amputees
Bibliographic record
Abstract
Research addressing lower limb amputee gait and prosthetic design often focuses on men, despite female lower limb amputees having different risk factors and lower success with their prosthetics overall. It is widely agreed that sex differences exist in able-bodied gait, but research analyzing sex differences in amputee gait is rare. This study compared male and female transtibial amputee gait to ascertain potential sex differences. Forty-five transtibial amputees were asked to walk at their self-selected speed, and spatiotemporal gait data were obtained. Both the mean and variability metric of parameters were analyzed for 10 male and 10 female participants. For all participants, amputated limbs had a shorter stance time, longer swing time, and larger step length. Females had a 10% shorter stance time and 26% larger normalized step and stride length than males. Female participants also walked over 20% faster than male participants. Finally, significant interactions were found in the mean and variability metric of stride velocity, indicating greater variability in women. These findings suggest that sex differences exist in transtibial amputee gait, offering possible explanations for the different comorbidities experienced by female lower limb amputees. These results have major implications for female amputees and for sex-specific research, rehabilitation, and prosthetic design.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".