Strength-Based Rehabilitation on Clinical Outcomes in Patients Postpartial Meniscectomy
Bibliographic record
Abstract
OBJECTIVE: The aim of the study is to determine the effect of strength-based rehabilitation programs on patients who underwent partial meniscectomy procedures. DESIGN: Three databases MEDLINE, PubMed, and Embase were searched from inception to May 9, 2022. Data on functional outcomes such as quadriceps and hamstring strengths, as well as patient-reported outcomes such as Knee Osteoarthritis and Outcome Score and visual analog scale were recorded. RESULTS: A total of nine studies comprising 417 patients were included in this review. There were no significant differences in quadriceps or hamstring strength measures between preinterventation and postintervention for both groups. Knee Osteoarthritis and Outcome Scores significantly increased from preintervention and postintervention in both control and exercise groups. Visual analog scale scores changed from 5.0 to 1.4 cm in the exercise arm and 3.1 to 1.9 cm in the control arm. CONCLUSIONS: Strength-based exercise programs for patients postpartial meniscectomy did not result in significantly different improvements in quadriceps or hamstring strength compared with control programs. However, strength-based exercise programs resulted in significantly higher Knee Osteoarthritis and Outcome Scores and a greater decrease in visual analog scale scores. Further studies, specifically well-designed systematic randomized controlled trials, are necessary to elucidate the specifics behind what type of exercises to use in addition to load progression and frequency of training.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".