Predicting post‐operative functional ability from pre‐operative measures in ACL‐injured individuals
Bibliographic record
Abstract
Purpose This study aimed to quantify the relationship between objective and subjective measures of functional ability and determine if measures in the deficient (ACLd) state were correlated to, and capable of predicting a patient's objective and subjective measures in the reconstructed (ACLr) state. Methods Twenty ACL‐injured participants completed hop and side cut movements prior to and 10 months post‐reconstruction. Their subjective measures (Tegner, Lysholm, IKDC, KOOS, and KNEEs) were related to objective measures of functional ability (peak knee flexion, peak knee extensor moment, stiffness, knee joint center excursion (KJCE), and knee joint center boundary). Correlations were used to determine relationships between variables whereas regressions were used to identify ACLd score's predictive ability of an ACLr score. Results Relationships between objective and subjective measures were task and ACL status dependent with KJCE and stiffness most commonly being related to subjective scores. The greatest correlation was between knee stiffness and Tegner in the ACLr group during the side cut (r = 0.69). Peak knee flexion angle (adj. R2 = 0.4‐0.66) was the best objective predictor between ACLd and ACLr states while KOOS‐ADL had the strongest correlations (r = 0.70‐0.77) and Tegner had the greatest predictive power (odds ratio: 1.46‐1.86) between states in both tasks. Conclusion Objective measures show a wide range of correlation to subjective measures with some being quite strong. Furthermore, objective measures in the ACLd state are more correlated and more often capable of predicting ACLr scores than the subjective measures of functional ability.
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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 | 0.003 |
| 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".