Use of Ultrasound to Monitor Biceps Femoris Mechanical Adaptations after Injury in a Professional Soccer Player.
Bibliographic record
Abstract
This study examined the use of ultrasound to monitor changes in the long head of the biceps femoris (BF) architecture of aprofessional soccer player with acute first-time hamstring strain. The player followed a 14 session physiotherapy treatment until return to sport. The pennation angle and aponeurosis strain of the long head of the biceps femoris (BF) were monitored at 6 occasions (up until 1 year) after injury. The size of the scar / hematoma was reduced by 63.56% (length) and 67.9% (width) after the intervention and it was almost non-traceable one year after injury. The pennation angle of the fascicles underneath the scar showed a decline of 51.4% at the end of the intervention while an increase of 109.2% of the fascicles which were closer to deep aponeurosis was observed. In contrast, pennation angle of fascicles located away from the injury site were relatively unaffected. The treatment intervention resulted in a 57.9% to 77.3% decline of maximum strain per unit of MVC moment and remained similar one year after the intervention. This study provided an example of the potential use of ultrasound-based parameters to link the mechanical adaptations of the injured muscle to specific therapeutic intervention. Key pointsChanges in fascicle orientation after biceps femoris mild tear were reduced after a 28 day intervention and remained similar one year after injury.Tendon/aponeurosis strain per unit of moment of force decreased during the course of the therapeutic intervention.Future studies could utilize ultrasonography to monitor mechanical responses after various types of hamstring injury and interventions in order to improve criteria for a safe return to sport.
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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 | 0.001 |
| 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 | 0.000 |
| 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".