Evidence map›Paper›PMID 40768443›Full record

ArticlePloS one2025

Application of mechanical quantitative techniques in postoperative rehabilitation assessment of anterior cruciate ligament reconstruction: A study protocol.

Zhichun Zhu, Jianyun Zhang, Wenxiang Ouyang, Tiantian Li, Jinsong Xiong, Mi Fan, Jing Guo, Peng Wang, Yongqing Tong, Yulu Yao

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Zhichun ZhuHunan Provincial Rehabilitation Hospital, Changsha Ctiy, Hunan Province, China.ORCID https://orcid.org/0000-0001-9322-1491
Jianyun ZhangHunan Provincial Rehabilitation Hospital, Changsha Ctiy, Hunan Province, China.
Wenxiang OuyangHunan Provincial Rehabilitation Hospital, Changsha Ctiy, Hunan Province, China.
Tiantian LiHunan Provincial Rehabilitation Hospital, Changsha Ctiy, Hunan Province, China.
Jinsong XiongHunan Provincial Rehabilitation Hospital, Changsha Ctiy, Hunan Province, China.
Mi FanHunan Provincial Rehabilitation Hospital, Changsha Ctiy, Hunan Province, China.
Jing GuoHunan Provincial Rehabilitation Hospital, Changsha Ctiy, Hunan Province, China.
Peng WangHunan Provincial Rehabilitation Hospital, Changsha Ctiy, Hunan Province, China.
Yongqing TongHunan Provincial Rehabilitation Hospital, Changsha Ctiy, Hunan Province, China.
Yulu YaoHunan Provincial Rehabilitation Hospital, Changsha Ctiy, Hunan Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study protocol aims to explore the application of mechanical quantitative techniques in the postoperative rehabilitation assessment following anterior cruciate ligament reconstruction (ACLR). Anterior cruciate ligament injuries are prevalent among athletes and pose a considerable threat to their careers. While ACLR remains the primary therapeutic intervention for such injuries, the assessment of postoperative rehabilitation still encounters significant challenges. Traditional assessment methods are limited by subjectivity and the absence of quantitative data. Mechanical quantitative techniques present a novel approach, offering objective and precise quantitative metrics for knee functional rehabilitation through the analysis of soft tissue mechanical properties.

methodsAn observational methodology will be employed to gather clinical data from patients with ACL injuries. A soft tissue mechanical quantitative measurement device will be utilized for knee functional assessment. The anticipated sample size is 66 cases, with inclusion criteria encompassing specific age ranges, injury durations, and other pertinent factors. Exclusion criteria will exclude participants with other injuries or diseases that may impact rehabilitation. The assessment protocol will consist of traditional knee functional assessments, including knee muscle strength, range of motion, and Lysholm scores, as well as mechanical quantitative assessments of the quadriceps and hamstrings. BLINDING: Due to the inherent challenges in clinical controlled trials, a strict double-blind design is infeasible. Therefore, participants will be aware of their group assignments, whereas assessors will remain blinded to the specific research objectives and group allocations. Data entry and statistical grouping will be managed by independent personnel. STATISTICAL ANALYSIS: Data analysis is conducted using SPSS 20.0 software. Categorical data are represented as frequencies (percentages), with intergroup comparisons made using chi-square tests or exact probability methods; continuous data are represented as means ± standard deviations, with intergroup comparisons made using t-tests, analysis of variance, or rank sum tests, and correlation analysis is performed using Spearman's correlation coefficient. A P-value <0.05 is considered statistically significant. EXPECTED

resultsFirstly, It is anticipated that there will be a significant correlation between the assessment results of mechanical quantitative techniques and traditional functional assessment methods. This will validate the reliability of mechanical quantitative techniques and facilitate their complementary use alongside traditional assessment methods. Secondly. It is expected that this study will develop new scientific measurement tools and methods, mitigating errors associated with subjective judgments and ensuring the objectivity and accuracy of assessment results. By providing specific, quantifiable data, this study will offer physicians a more intuitive and precise basis for assessment.

Indexed as

Anterior Cruciate Ligament InjuriesAnterior Cruciate Ligament ReconstructionAdultAnterior Cruciate LigamentBiomechanical PhenomenaFemaleHumansKnee JointMaleMuscle StrengthRange of Motion, ArticularYoung Adult

Identifiers

PMID40768443
PMCPMC12327682

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.