{
  "abstract": "Introduction: Phenotypic profiling of elite athletes remains constrained by supervised frameworks that conflate physiological characteristics with performance outcomes. Unsupervised machine learning offers an outcome-independent alternative for identifying intrinsic athlete profiles.\n\nMethod: This study applied clustering algorithms to comprehensive laboratory data from elite flatwater canoeists. Twenty-one international-level C1 canoeists (12 male, 9 female) underwent a standardized testing battery including body composition, muscle contractile properties, mobility, maximal and explosive strength, and sport-specific neuromuscular assessments via Functional Electromechanical Dynamometry (FEMD). All performance variables were excluded to ensure purely phenotypic segmentation. Preprocessing included asymmetric winsorization, Yeo-Johnson transformation, and robust scaling. K-Means and hierarchical clustering (Ward linkage) were applied with optimal k determined via silhouette coefficient, Davies-Bouldin index, and elbow method. Discriminant variable importance was assessed via Random Forest Classifier; interpretable classification rules were extracted using Decision Tree analysis (max_depth=3) with stratified train-test validation (66.7%–33.3%). Statistical characterization of cluster differences employed ANOVA or Kruskal-Wallis tests with post-hoc comparisons and Benjamini-Hochberg FDR correction; algorithmic concordance was quantified via Adjusted Rand Index (ARI).\n\nResults: Three phenotypic clusters emerged: High-Neuromuscular Male Phenotype (n=7, 33.3%), Female-Dominant Phenotype (n=10, 47.6%), and Moderate-Neuromuscular Male Phenotype (n=4, 19.0%) (ARI = 0.591). Sex stratification was marked (χ²=18.90, p<0.001): the Female-Dominant Phenotype comprised 90% female athletes; both male phenotypes were exclusively male, encoding sexual dimorphism alongside intra-sex neuromuscular differentiation. FEMD neuromuscular variables dominated discrimination (75.9% of top-15 importance; 14 of 15 variables with padj < 0.01 following Benjamini-Hochberg FDR correction). Decision Tree classification achieved 100% training accuracy and 71.4% test accuracy (5/7; 95% CI: 29.0%–96.3%; p=0.045 vs. chance), though the small test partition constrains generalizability. Within male phenotypes, Clusters 0 and 2 demonstrated equivalent neuromuscular profiles (all p > 0.05), with phenotypic separation captured by latissimus dorsi sustain time from the non-paddle side; differences relative to the Female-Dominant Phenotype primarily reflect sexual dimorphism.\n\nDiscussion: Unsupervised clustering suggests a tripartite phenotypic structure in elite canoeists consistent with sexual dimorphism and intra-sex neuromuscular differentiation, with FEMD-derived eccentric force as primary discriminant. Classification rules may inform future sex-stratified talent identification and training individualization; external validation with larger cohorts is required.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Physical Education and Sports, Faculty of Sports Sciences and Physical Education, University of A Coruña , ,",
    "Sport Performance, Physical Condition and Wellness lab, Faculty of Education and Sports Sciences, University of Vigo , ,",
    "Department of Design in Engineering, School of Industrial Engineering, University of Vigo , ,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Tania Álvarez-Yates"
    },
    {
      "affs": [
        1
      ],
      "name": "Óscar García-García"
    },
    {
      "affs": [
        1
      ],
      "name": "Alba Cuba-Dorado"
    },
    {
      "affs": [
        2
      ],
      "name": "José-Benito Bouza-Rodríguez"
    },
    {
      "affs": [
        2
      ],
      "name": "Alberto Comesaña-Campos"
    },
    {
      "affs": [
        2
      ],
      "name": "Jorge Cerqueiro-Pequeño"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6wza88v10cnnq.bib",
  "cited_by": 0,
  "doi": "10.3389/fphys.2026.1949031",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13725060/?tool=EBI",
  "id": "6wza88v10cnnq",
  "issns": [
    "1664-042X"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13725060",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Research"
    ],
    [
      "openalex",
      "2732|Sports Performance and Training"
    ]
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  "openalex": "W7222933225",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13725060/pdf/?tool=EBI",
  "pmcid": "PMC13725060",
  "pmid": "42974027",
  "published": "2026-10-19",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/6wza88v10cnnq.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T15:20:51Z",
      "native_id": "PMC:PMC13725060",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13725060"
    },
    {
      "first_seen": "2026-11-04T12:23:31Z",
      "native_id": "PMC13725060",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fphys.2026.1949031"
    },
    {
      "first_seen": "2026-11-04T15:20:48Z",
      "native_id": "MED:42974027",
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  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2732|Sports Performance and Training",
      "subfield": "surgery"
    }
  ],
  "title": "Unsupervised phenotypic clustering, inferential characterization, and discriminant classification of elite flatwater canoeists: neuromuscular and anthropometric determinants",
  "title_text": "Unsupervised phenotypic clustering, inferential characterization, and discriminant classification of elite flatwater canoeists: neuromuscular and anthropometric determinants",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T12:21:24Z",
  "url": "https://research.draconitic.com/paper/6wza88v10cnnq",
  "venue": "Frontiers in physiology",
  "versions": []
}