Knee Angular Impulse as a Predictor of Patellofemoral Pain in Runners
Bibliographic record
Abstract
BACKGROUND: Identification of mechanical factors associated with patellofemoral pain, the most prevalent running injury, is necessary to help in injury prevention, but unfortunately they remain elusive. HYPOTHESIS: Runners who develop patellofemoral pain have increased knee joint angular impulse in the frontal plane. STUDY DESIGN: Case control study; Level of evidence, 3. METHODS: A retrospective study compared knee abduction impulses of 20 patellofemoral pain patients with those of 20 asymptomatic patients. A second prospective study quantified knee angular impulses during the stance phase of running of 80 runners at the beginning of the summer running season. Epidemiologic data were then collected, recording the type and severity of injury of these runners during a 6-month running period. RESULTS: The patellofemoral pain patients in the retrospective study had significantly higher (P = .026) knee abduction impulses (17.0 +/- 8.5 Nms) than did the asymptomatic patients (12.5 +/- 5.5 Nms). Six patients developed patellofemoral pain during the prospective study. The prospective data showed that patients who developed patellofemoral pain had significantly higher (P = .042) knee abduction impulses (9.2 +/- 3.7 Nms) than did matched patients who remained uninjured (4.7 +/- 3.5 Nms). CONCLUSION: The data indicate that increased knee abduction impulses should be deemed risk factors that play a role in the development of patellofemoral pain in runners. CLINICAL RELEVANCE: Footwear and running style can influence knee angular impulse, and the appropriate manipulation of these variables may play a preventive role for patients who are predisposed to patellofemoral pain.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".