Association Between Changes in Subjective and Objective Measures of Mobility in People With Lower Limb Amputations After Inpatient Rehabilitation
Bibliographic record
Abstract
Functional recovery for people with lower limb amputations is quantified using objective or subjective measures of performance. In this brief report, the prospective relationship between objective and subjective mobility after rehabilitation was evaluated in people with lower limb amputations. Adults undergoing inpatient prosthetic rehabilitation for a first unilateral transtibial or transfemoral level lower limb amputation were recruited. Assessment times: discharge and 4-mo follow-up. Gait velocity and the L Test under single- and dual-task conditions measured objective mobility. The Prosthetic Evaluation Questionnaire (section 4 and question 5b) measured subjective mobility. Paired t tests and Pearson correlation analysis evaluated change over time and the association between mobility types, respectively. Twenty-one people with lower limb amputations (61.6 ± 8.2 yrs) participated. Gait velocity significantly improved (single- and dual-task: P < 0.001). L Test significantly improved for single-task (P = 0.002) but not dual-task conditions. No statistically significant Prosthetic Evaluation Questionnaire changes were observed. One subjective mobility question (sidewalk walking) correlated with objective mobility at follow-up (L Test single- and dual-task: r = -0.77; P < 0.001). Objective mobility improved after discharge; however, subjective reporting had no change. Lack of association may represent a mismatch between quantitative outcomes and subjective self-assessment. Both subjective and objective measures of mobility should be collected to provide a holistic picture of clinical and patient-relevant outcomes in people with lower limb amputations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".