Tracking Patient Outcomes after Anterior Cruciate Ligament Reconstruction
Bibliographic record
Abstract
UNLABELLED: Purpose : To model how patients' knee range of motion (ROM), pain, and self-reported lower-extremity (LE) functional status change over the first 26 weeks following anterior cruciate ligament (ACL) reconstruction and to estimate the test-retest reliability of these measurements. METHODS: Patients were assessed weekly over 26 weeks following ACL reconstruction. Outcomes were knee ROM, LE functional status measured by the Lower Extremity Functional Scale (LEFS), and pain measured by the 4-item pain intensity measure (P4). A nonlinear model was applied to describe change for each outcome. Intra-class correlation coefficients and standard errors of measurement were applied to estimate test-retest reliability and minimal detectable change. RESULTS: A nonlinear model provided the following model fit values (R(2)): P4=0.71, extension ROM=0.51, flexion ROM=0.99, LEFS=0.97. For pain and ROM, the limit values were reached by approximately 12 weeks after reconstruction; LEFS values continued to increase up to 26 weeks. Test-retest reliability coefficients varied from 0.85 to 0.95. CONCLUSIONS: The greatest improvement occurred in the first 8 weeks after surgery. Recovery was nearly complete by 12 weeks with respect to pain and ROM, although LE functional status continued to improve throughout the study period. Scores on all measures demonstrated reliability, which supports their use with individual patients.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".