Evidence map›Paper›PMID 35941323›Full record

ArticleKnee surgery, sports traumatology, arthroscopy : official journal of the ESSKA2022

Pre-injury performance is most important for predicting the level of match participation after Achilles tendon ruptures in elite soccer players: a study using a machine learning classifier.

Pedro Diniz, Mariana Abreu, Diogo Lacerda, António Martins, Hélder Pereira, Frederico Castelo Ferreira, Gino Mmj Kerkhoffs, Ana Fred

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Article in Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
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

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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

Who cites it

7 citing papers in PubMed.

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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

8 authors.

Pedro DinizDepartment of Orthopaedic Surgery, Hospital de Sant'Ana, Rua de Benguela, 501, 2775-028, Parede, Portugal. pedro.diniz@scml.pt.ORCID http://orcid.org/0000-0001-9234-7041
Mariana AbreuDepartment of Bioengineering and iBB, Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
Diogo LacerdaDepartment of Orthopaedic Surgery, Hospital de Sant'Ana, Rua de Benguela, 501, 2775-028, Parede, Portugal.
António MartinsDepartment of Orthopaedic Surgery, Hospital de Sant'Ana, Rua de Benguela, 501, 2775-028, Parede, Portugal.
Hélder PereiraOrthopaedic Department, Centro Hospitalar Póvoa de Varzim, Vila do Conde, Portugal.
Frederico Castelo FerreiraDepartment of Bioengineering and iBB, Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
Gino Mmj KerkhoffsDepartment of Orthopaedic Surgery, Amsterdam Movement Sciences, Amsterdam University Medical Centers, Amsterdam, The Netherlands.
Ana FredDepartment of Bioengineering and iBB, Institute for Bioengineering and Biosciences, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeAchilles tendon ruptures (ATR) are career-threatening injuries in elite soccer players due to the decreased sports performance they commonly inflict. This study presents an exploratory data analysis of match participation before and after ATRs and an evaluation of the performance of a machine learning (ML) model based on pre-injury features to predict whether a player will return to a previous level of match participation.

methodsThe website transfermarkt.com was mined, between January and March of 2021, for relevant entries regarding soccer players who suffered an ATR while playing in first or second leagues. The difference between average minutes played per match (MPM) 1 year before injury and between 1 and 2 years after the injury was used to identify patterns in match participation after injury. Clustering analysis was performed using k-means clustering. Predictions of post-injury match participation were made using the XGBoost classification algorithm. The performance of this model was evaluated using the area under the receiver operating characteristic curve (AUROC) and Brier score loss (BSL).

resultsTwo hundred and nine players were included in the study. Data from 32,853 matches was analysed. Exploratory data analysis revealed that forwards, midfielders and defenders increased match participation during the first year after injury, with goalkeepers still improving at 2 years. Players were grouped into four clusters regarding the difference between MPMs 1 year before injury and between 1 and 2 years after the injury. These groups ranged between a severe decrease (n = 34; - 59 ± 13 MPM), moderate decrease (n = 75; - 25 ± 8 MPM), maintenance (n = 70; 0 ± 8 MPM), or increase (n = 30; 32 ± 13 MPM). Regarding the predictive model, the average AUROC after cross-validation was 0.81 ± 0.10, and the BSL was 0.12, with the most important features relating to pre-injury match participation.

conclusionMost players take 1 year to reach peak match participation after an ATR. Good performance was attained using a ML classifier to predict the level of match participation following an ATR, with features related to pre-injury match participation displaying the highest importance. LEVEL OF EVIDENCE: I.

Indexed as

Achilles TendonAnkle InjuriesAthletic PerformanceSoccerTendon InjuriesHumansMachine LearningAchilles tendonEpidemiologyFootball (soccer)General sports traumaMachine learningStatistics

Identifiers

PMID35941323
PMCPMC9360634

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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.