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Record W3033633715 · doi:10.1016/j.ijsu.2020.05.035

Arthroscopic partial meniscectomy combined with medical exercise therapy versus isolated medical exercise therapy for degenerative meniscal tear: A meta-analysis of randomized controlled trials

2020· review· en· W3033633715 on OpenAlexaboutno aff
Hua-gang Pan, Peng Zhang, Zhaodong Zhang, Quan Yang

Bibliographic record

VenueInternational Journal of Surgery · 2020
Typereview
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRandomized controlled trialMeta-analysisMedical therapyExercise therapyPhysical therapySports medicineSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Degenerative meniscal tear is a chronic disorder which presents with knee pain, swelling and loss of motion. It is currently unknown whether arthroscopic partial meniscectomy combined with medical exercise therapy is superior to isolated medical exercise therapy for degenerative meniscal tear. OBJECTIVE: To determine if medical exercise therapy alone is as effective as arthroscopic partial meniscectomy combined with medical exercise therapy in treating degenerative meniscal tear. METHOD: Electronic searches were performed using MEDLINE, EMBASE, and the Cochrane Library Databases for all randomized studies. Two reviewers independently completed the literature screening, data extraction, and risk evaluation of bias. The outcome measures were visual analogue scale (VAS), the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), range of motion, the Lysholm Knee Scoring Scale (LKSS), Knee injury and Osteoarthritis Outcome Score (KOOS) and postoperative complications. STATA 13.0 software was applied for meta-analysis. RESULT: Six randomized controlled trials (RCTs) were conducted, with 900 patients included. The present study revealed that there were significant differences between the two groups regarding the VAS at two months, as well as, WOMAC and range of motion. No significant differences were found in terms of LKSS, KOOS or postoperative complications. LIMITATIONS: (1) Only 6 RCTs were included in our meta-analysis and the sample sizes were small; (2) The follow-up period was too short in some included studies. Long-term follow-up studies should be conducted in the future; (3) Heterogeneity among the included studies was unavoidable due to different grade of degenerative meniscal tear and program of exercise. Heterogeneity was also caused by a variety of other factors. (4) Publication bias that came from the process of literature searching was unavoidable and was hard to overcome. (5) There are many other words which could yielded more studies (Ex. physiotherapy, physical therapy modalities, exercise therapy, rehabilitation, knee, placebo, groups, tibial meniscus, meniscus, arthroscopy, meniscectomy, partial meniscectomy, randomized controlled trial, controlled clinical trial, randomized, systematic review, and meta-analysis). Implications of key findings: This meta-analysis suggests that doctors can choose arthroscopic partial meniscectomy combined with medical exercise therapy for the treatment of degenerative meniscal tear. CONCLUSION: Arthroscopic partial meniscectomy combined with medical exercise therapy is effective in reducing pain and improving range of motion in the early postoperative period. Therefore, arthroscopic partial meniscectomy combined with medical exercise therapy may be recommended for the treatment of degenerative meniscal tear. Further research is necessary to determine the type, frequency, and duration of the best exercise program. Systematic review registration number: Reviewregistry884.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.030
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.036
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0300.058
Bibliometrics0.0070.007
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.148
GPT teacher head0.425
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations28
Published2020
Admission routes1
Has abstractyes

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