Functional Outcomes of Proximal Femur Limb Salvage Surgery
Bibliographic record
Abstract
Sarcoma cancer of the proximal femur is a bone tumor that develops near the hip joint. The most common method of treatment is limb salvage surgery (LLS), a highly invasive surgery that often leads to impaired movement including walking due to soft tissue resection. The current thesis focuses on 1) systematically reviewing current literature of functional outcomes after proximal femur LSS to determine if specific methods of muscle reattachment lead to better limb function, and 2) objectively analysing how reducing hip muscle strength impacts one’s ability to achieve healthy gait. Findings from the systematic review suggest using artificial mesh or ligaments for LLS may be a good alternative to allograft prosthesis composites and trochanter osteotomy, producing good functional outcomes with low rates of complications. It was also determined current literature is lacking objective quantitative analysis of patients’ limb function after surgery. Objective 2 was executed using instrumented gait analysis to record the gait kinematics, kinetics and EMG patterns of a patient who received LSS for proximal femur sarcoma. Data from the gait analysis was used to create a patient-specific musculoskeletal model. Healthy gait kinematics were applied to the model and specific hip muscle strengths were systematically reduced to simulate different surgical interventions. After an 85% reduction in gluteus medius and minimus muscle strength, healthy gait kinematics were not achieved. Reducing muscle strength of the gluteus medius and minimus together had a greater impact on the model’s ability to achieve healthy gait kinematics then when reduced individually. An understanding of how patient’s limb function is impacted after surgery can inform surgical technique, implant design and physiotherapy programs leading to better quality of life for patients after surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".