{
  "abstract": "Background Patients with chronic ankle instability (CAI) face a 2-year reinjury rate approaching 50%, but individualized prediction models that quantify which baseline impairments predict subsequent reinjury within an established CAI cohort remain scarce, and existing prediction models for ankle injury have not assessed sex-related fairness. The aim was to develop and internally validate sex-equitable machine learning (ML) models predicting 2-year ankle reinjury in individuals with CAI, identify the most influential baseline prognostic markers, and audit sex-stratified model performance. Methods One hundred physically active individuals (50 male, 50 female) with CAI (mean age 21.8 ± 2.3 years) were recruited from a university population between February 2017 and February 2018 and prospectively followed for 2 years; 96 (49 male, 47 female) with at least one follow-up response were retained for analysis (41 reinjury events, 43% reinjury rate). Fourteen baseline predictors spanning demographic, anthropometric, injury-history, patient-reported, biomechanical, postural-control, and functional-performance domains were collected. Six algorithms were trained with nested cross-validation: an unpenalized logistic regression baseline and five ML algorithms (Elastic Net, support vector machine, random forest, XGBoost, LightGBM). Predictive contribution of each feature was quantified using Shapley additive explanations and partial-dependence plots. A pre-specified fairness audit compared single global and sex-specific Youden thresholds derived out-of-fold using the equalized-odds gap and disproportionate-impact ratio. Results All algorithms achieved area under the curve (AUC) values above 0.70; Elastic Net was selected as the primary model (AUC = 0.807, 95% CI 0.714–0.893; calibration slope = 1.015). Sex, arch height index, previous sprains, dorsiflexion range of motion, and Biodex Overall Stability Index consistently emerged as the most influential prognostic markers. Within-sex discrimination was comparable between males and females, but a single global threshold produced an equalized-odds gap of 0.384 that was reduced by 57% (to 0.164) through sex-specific thresholding without retraining the model. Conclusions ML models with a fairness-audited threshold strategy predicted 2-year ankle reinjury in individuals with CAI, reducing the equalized-odds gap by 57%, though the disproportionate-impact ratio (0.683) remained below the 0.80 benchmark. External validation is required before clinical deployment.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Rehabilitation Sciences, The Hong Kong Polytechnic University",
    "Graduate School of Sports Medicine, CHA University",
    "Department of Exercise Sciences, Brigham Young University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ui-jae Hwang"
    },
    {
      "affs": [
        1
      ],
      "name": "S. Jun Son"
    },
    {
      "affs": [
        0
      ],
      "name": "Siu-ngor Fu"
    },
    {
      "affs": [
        0
      ],
      "name": "Arnold Y. L. Wong"
    },
    {
      "affs": [
        2
      ],
      "name": "Dustin Bruening"
    },
    {
      "affs": [
        2
      ],
      "name": "Brent Feland"
    },
    {
      "affs": [
        2
      ],
      "name": "Matthew Seeley"
    },
    {
      "affs": [
        2
      ],
      "name": "J. Ty Hopkins"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b2c7gscsqc81t.bib",
  "cited_by": 0,
  "doi": "10.1186/s40798-026-01137-3",
  "fulltext_url": "https://doi.org/10.1186/s40798-026-01137-3",
  "id": "b2c7gscsqc81t",
  "issns": [
    "2198-9761"
  ],
  "landing_url": "https://doi.org/10.1186/s40798-026-01137-3",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "RC1200-1245"
    ],
    [
      "openalex",
      "2732|Foot and Ankle Surgery"
    ]
  ],
  "openalex": "W7225449358",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-08",
  "publisher": "SpringerOpen",
  "ris": "https://research.draconitic.com/paper/b2c7gscsqc81t.ris",
  "sources": [
    {
      "first_seen": "2026-11-08T14:20:57Z",
      "native_id": "11871ea757bf46bcae9247cdda029aa0",
      "source": "doaj",
      "url": "https://doi.org/10.1186/s40798-026-01137-3"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2732|Foot and Ankle Surgery",
      "subfield": "surgery"
    }
  ],
  "title": "Sex-Equitable Machine Learning Prediction of Ankle Reinjury in Individuals with Chronic Ankle Instability: A 2-Year Prospective Cohort Study with Fairness Audit",
  "title_text": "Sex-Equitable Machine Learning Prediction of Ankle Reinjury in Individuals with Chronic Ankle Instability: A 2-Year Prospective Cohort Study with Fairness Audit",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T14:21:17Z",
  "url": "https://research.draconitic.com/paper/b2c7gscsqc81t",
  "venue": "Sports Medicine - Open",
  "versions": []
}