Muscle strength characteristics following megaprosthetic knee reconstruction for bone sarcoma
Bibliographic record
Abstract
OBJECTIVE: To assess muscle strength characteristics in patients with resection and megaprosthetic reconstruction of the knee for bone sarcoma compared to age- and sex-matched controls. METHODS: This was a cross-sectional, case-control study. Muscle strength characteristics for knee extension and -flexion were assessed isokinetically at three different joint velocities: 60, 120 and 180°/s, and by the rate of force development (RDFmax) in knee extension. The Toronto Extremity Salvage Score (TESS) was used in patients. RESULTS: Eighteen patients (91.6 months postop.) and 18 controls were included. Relative to controls, patients generated maximal torques of 19%, 23% and 23% in knee extension at 60, 120 and 180°/s, respectively. For knee flexion, patients generated maximal torques of 58%, 53% and 60% at 60, 120, and 180°/s, relative to the controls. RDFmax of the operated leg was 2.75 ± 2.13 N/ms, 7.16 ± 4.78 N/ms for the non-operated leg, and 7.95 ± 4.29 N/ms for the controls. The mean TESS score was 84.0. CONCLUSION: Patients reached approximately 20% of the maximal knee extension torque. In isometric assessments, they used double the amount of time to generate one-third of the maximal force compared to the controls despite good TESS scores.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".