Relationship of Knee Forces to Subjective Function Pre- and Post-ACL Reconstruction
Bibliographic record
Abstract
PURPOSE: Although basic objective measures (e.g., knee laxity, strength, and hop tests) have been related to subjective measures of function, associations between knee-specific objective and subjective measures have yet to be completed. The objective was to determine if knee joint contact and ligament forces differ between pre- and post-anterior cruciate ligament (ACL) reconstructed states and if these forces relate to their patient's respective subjective functional ability scores. METHODS: Twelve patients performed a hopping task before and after reconstruction. Magnetic resonance images and OpenSim were used to develop patient-specific models in static optimization and joint reaction analyses. Questionnaires concerning each patient's subjective functional ability were also collected and correlated with knee joint contact and ligament forces. RESULTS: No significant differences were observed between deficient and reconstructed groups with respect to knee joint contact or ligament forces. Nevertheless, there were several significant (P < 0.05) moderate to strong correlations between subjective and objective measures including Tegner activity level to contact force in both states (r = 0.67-0.76) and International Knee Documentation Committee to compressive and anterior shear forces (r = 0.64-0.66). CONCLUSION: Knee-specific objective measures of a patient's functional capacity can represent their subjective ability, which explains this relationship to a greater extent than past anatomical and gross objective measures of function. This consolidation is imperative for improving the current rehabilitation schema as it allows for external validation of objective and subjective functional measures. With poor validation of subjective function against objective measures of function, the reinjury rate is unlikely to diminish, continuing the heavy financial burden on health care systems.
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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 | 0.001 | 0.005 |
| 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) | 0.002 | 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".