Longitudinal Changes in the Lower Extremity Functional Scale After Anterior Cruciate Ligament Reconstructive Surgery
Bibliographic record
Abstract
OBJECTIVE: To describe the pattern of change in lower extremity physical function status as measured by the Lower Extremity Functional Scale (LEFS) during the first 16 weeks after anterior cruciate ligament (ACL) reconstructive surgery and illustrate how this information can be applied in clinical practice to assist with goal setting and the evaluation of patient outcomes. The secondary objective is to estimate the test-retest reliability of the LEFS in this population. DESIGN: Prospective cohort, observational. SETTING: Physiotherapy private practice. PATIENTS: Forty-seven participants underwent ACL reconstructive surgery and were initially recruited. Two participants were excluded from the analysis, resulting in 45 participants (28 men, mean age 29.4 years; 17 women, mean age 29.0 years). INTERVENTIONS: Participants underwent a rehabilitation protocol. MAIN OUTCOME MEASURES: Participants completed the LEFS at each visit from their initial physiotherapy session to 16 weeks postsurgery. A nonlinear model of change was developed, which related LEFS scores to weeks postsurgery. Test-retest reliability was examined between the seventh and ninth weeks using intraclass correlation coefficients (ICC2,1) and standard error of measurement (SEM). RESULTS: The nonlinear model demonstrated rapid improvements in LEFS scores within the first 7 to 8 weeks with a gradual tapering of this improvement. At 16 weeks, the predicted LEFS score was 63 out of a maximum score of 80. The LEFS demonstrated excellent test-retest reliability in this population (ICC2,1 = 0.90, SEM = 3.7). CONCLUSIONS: This study provides a description of postsurgical change in functional status for patients after ACL reconstructive surgery that can assist clinicians in developing clinical goals. CLINICAL RELEVANCE: A rapid improvement in lower extremity physical function is demonstrated in the first 7 to 8 weeks after ACL reconstructive surgery with a tapering of this improvement after 8 weeks.
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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 | 0.003 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".