Jump-Landing Mechanics Assessment Using Landing Error Scoring System in Athletes with and without Patellofemoral Pain:
Bibliographic record
Abstract
Abstract Purpose: This study was a cross-sectional study that aimed to compare the total LESS scores of individuals with PFP with healthy controls and assess the association of pain, function, and psychological factors with LESS score. Methods: Twenty-seven male athletes with PFP completed a standardized jump-landing task. They were compared with a matched, healthy group. Also, participants completed four questionnaires involving the visual analog scale (VAS), Anterior knee pain scale (AKPS), fear of motion (TAMPA), beck anxiety and depression inventory scale (BAI, BDI). Results: PFP group had a higher total LESS score than the control group significantly. They had errors when landing with lateral trunk flexion and less knee flexion in the initial contact. Our results showed a significantly strong correlation between VAS, AKPS, and TAMPA with a final score LESS. Also, a low to moderate significant correlation obtained between BAI, BDI and final score LESS. Conclusions: The LESS is a useful clinical test for evaluating landing errors in people with PFP. Greater kinesiophobia, pain, poorer self-reported function and psychological factors was correlated with a total LESS score.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".