Gait Kinematics in Individuals with Acute and Chronic Patellofemoral Pain
Bibliographic record
Abstract
PURPOSE: This study aimed to identify the discriminating kinematic gait characteristics between individuals with acute and chronic patellofemoral pain (PFP) and healthy controls. METHODS: Ninety-eight runners with PFP (39 male, 59 female) and 98 healthy control runners (38 male, 60 female) ran on a treadmill at a self-selected speed while three-dimensional lower limb kinematic data were collected. Runners with PFP were split into acute (n = 25) and chronic (n = 73) subgroups on the basis of whether they had been experiencing pain for less or greater than 3 months, respectively. Principal component analysis and linear discriminant analysis were used to determine the combination of kinematic gait characteristics that optimally separated individuals with acute PFP and chronic PFP and healthy controls. RESULTS: Compared with controls, both the acute and chronic PFP subgroups exhibited greater knee flexion across stance and greater ankle dorsiflexion during early stance. The acute PFP subgroup demonstrated greater transverse plane hip motion across stance compared with healthy controls. In contrast, the chronic PFP subgroup demonstrated greater frontal plane hip motion, greater knee abduction, and reduced ankle eversion/greater ankle inversion across stance when compared with healthy controls. CONCLUSIONS: This study identified characteristics that discriminated between individuals with acute and chronic PFP when compared with healthy controls. Certain discriminating characteristics were shared between both the acute and chronic subgroups when compared with healthy controls, whereas others were specific to the duration of PFP.
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 | 0.002 |
| 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 | 0.000 | 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".