Influence of Exercise-Induced Injury on Knee Extension Torque in the Presence of Long-Standing Quadriceps Atrophy: Case Report
Bibliographic record
Abstract
Purpose: The purposes of this case report were (1) to describe muscle volume and torque-generating capacity of the quadriceps muscle in a 46-year-old woman 16 years following tibial plateau fracture and (2) to compare the decline and recovery of quadriceps torque following exerciseinduced muscle damage between the affected and unaffected lower extremity (LE). Methods: Magnetic resonance imaging was used to determine quadriceps volume. Repeated measures of concentric, eccentric, and isometric torque and muscle soreness were acquired for quadriceps bilaterally during four baseline tests, as well as 2 hours and 1, 2, 11, 12, and 13 days after exerciseinduced muscle damage (eccentric exercise). Results: At baseline, the affected quadriceps showed profound atrophy (74% of unaffected quadriceps volume) and deficits in all torque measures. Exercise-induced muscle damage resulted in slight delayed-onset muscle soreness and a decrease in all torque measures bilaterally. In the affected LE at 13 days after exercise, eccentric torque did not recover and isometric torque on the affected side recovered only partially. Conclusions: Quadriceps atrophy and torque deficit can be substantial long after tibial plateau fracture. Eccentric torque was the most sensitive measure of torque deficit, showing only partial recovery after exercise-induced muscle damage.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".