Novel application of lower body positive-pressure in the rehabilitation of an individual with multiple lower extremity fractures
Bibliographic record
Abstract
OBJECTIVE: Lower extremity fractures, if not treated appropriately, can increase the risk of morbidity. Partial weight-bearing after surgical repair is recommended; however, current methods of partial weight-bearing may cause excessive loads through the lower extremity. A new rehabilitation tool that uses lower body positive-pressure is described, that may allow partial weight-bearing while preventing excessive loads, thereby improving functional outcomes. METHODS: A patient with multiple lower extremity fractures underwent a 6-month rehabilitation programme using bodyweight support technology 3 times per week, post-surgery. RESULTS: The patient experienced a reduction in pain and an improvement in ankle range of motion (p=0.002), walking speed (p>0.05) and physical function (p=0.004), as assessed by the Foot and Ankle Module of the American Academy of Orthopaedic Surgeons Lower Limb Outcomes Assessment Instrument. Training did not appear to affect fracture healing, as was evident on radiograph. The effect of lower body positive-pressure on effusion, which has not previously been reported in the literature, was also investigated. No significant difference in effusion of the foot and ankle when using lower body positive-pressure was found. CONCLUSION: Initial results suggest that this new technology may be a useful rehabilitation tool that allows partial weight-bearing during the treatment of lower extremity injuries.
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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 | 0.000 |
| 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 | 0.001 | 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".