{
  "id": "AIBD-0221",
  "title": "Dytective gamified online dyslexia screener reaches 80.6% recall on 3,644 Spanish-speaking children (PLoS ONE 2020); a 2026 re-analysis finds a demographic confound",
  "summary": "Rello et al.'s random-forest screener, tested on 3,644 children from 113 organizations in five countries, 79.8% accuracy; the tool has been used over 200,000 times. A 2026 paper reports confounding.",
  "date_observed": "2020-12-02",
  "date_basis": "unassessed",
  "date_precision": "unassessed",
  "benefit_status": null,
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  "account": "Luz Rello (IE University, Madrid), Baeza-Yates, Ali, Bigham and Serra published in PLoS ONE (2 December 2020) a web-based gamified test of 32 linguistic exercises. Its performance measures feed a random-forest classifier with 200 trees. The study enrolled 3,644 participants aged 7–17, 392 with a professional dyslexia diagnosis, recruited through 113 organizations in Argentina, Chile, Colombia, Spain and the USA. They were 60 primary and secondary schools, 22 specialized dyslexia centers, 18 non-profits and 3 universities. The classifier reached 79.8% accuracy with 80.6% recall and 79.3% precision for the dyslexia class. A tablet validation with 1,395 participants aged 12–17 gave 78.1% recall and 76.2% precision. The authors state that an online Spanish screening tool based on their methods had already been used by more than 200,000 people. They disclose a patent application (US 15/493,060, Rello and Ballesteros). Sudha and Murali (Frontiers in Digital Health, 23 September 2026) re-analyzed the same 3,644-participant dataset. They report that dyslexia prevalence in the dataset differs 4.7-fold by native-language status. Four demographic columns alone reach AUC 0.724 against 0.744 for all 192 task metrics. Including them yields a twelve-fold disparity in false-positive rate between language groups.",
  "category": "general benefit",
  "unexpected": false,
  "purpose": "Dytective was designed to screen children for dyslexia risk cheaply online. The accuracy figures and usage are that purpose.",
  "system": "Dytective online gamified dyslexia screening test with a random-forest classifier",
  "model_version": "Weka 3.8.3 implementation with 200 trees and unlimited height",
  "developer": "Luz Rello and co-authors (IE University, Northeastern, University of Washington, Carnegie Mellon, University of Barcelona), per the paper's author list",
  "deployer": "Schools, dyslexia centers and non-profits in Argentina, Chile, Colombia, Spain and the USA (study participants); public online use in Spanish-speaking countries",
  "location": null,
  "domain": "Education / learning-disability screening",
  "segment": "School-age dyslexia screening",
  "benefit_type": "Improved Task Accuracy",
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    "statement": "The random-forest classifier is the screening decision. The 2026 re-analysis shows that demographic variables alone reach nearly the same AUC in this dataset and that false-positive rates differ twelve-fold between language groups. So the measured accuracy may partly reflect sample composition."
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  "evidence": [
    {
      "kind": "peer-reviewed study",
      "citation": "Rello L, Baeza-Yates R, Ali A, Bigham JP, Serra M (2020). Predicting risk of dyslexia with an online gamified test. PLoS ONE 15(12): e0241687 (PMC7710040).",
      "title": "Predicting risk of dyslexia with an online gamified test",
      "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC7710040/",
      "date": "2020-12-02",
      "written_by": "makers",
      "quote": "An online screening tool in Spanish based on our methods has already been used by more than 200,000 people."
    },
    {
      "kind": "peer-reviewed study",
      "citation": "Sudha M, Murali S (2026). GRAPHOS re-examined: graph-theoretic features do not improve dyslexia screening, and a demographic confound in the Dytective dataset. Frontiers in Digital Health 8; doi 10.3389/fdgth.2026.1927725 (PMC13656794).",
      "title": "GRAPHOS re-examined: graph-theoretic features do not improve dyslexia screening, and a demographic confound in the Dytective dataset",
      "url": "https://europepmc.org/article/PMC/PMC13656794",
      "date": "2026-09-23",
      "written_by": "independent",
      "quote": "dyslexia prevalence in this dataset differs 4.7-fold by native-language status"
    },
    {
      "kind": "peer-reviewed study",
      "citation": "Rello L, Baeza-Yates R, Ali A, Bigham JP, Serra M (2020). Predicting risk of dyslexia with an online gamified test. PLoS ONE 15(12): e0241687 (publisher page).",
      "title": "Predicting risk of dyslexia with an online gamified test",
      "url": "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0241687",
      "date": "2020-12-02",
      "written_by": "makers",
      "quote": null
    },
    {
      "kind": "peer-reviewed study",
      "citation": "Sudha M, Murali S (2026). GRAPHOS re-examined: graph-theoretic features do not improve dyslexia screening, and a demographic confound in the Dytective dataset. Frontiers in Digital Health 8; doi 10.3389/fdgth.2026.1927725 (publisher page).",
      "title": "GRAPHOS re-examined: graph-theoretic features do not improve dyslexia screening, and a demographic confound in the Dytective dataset",
      "url": "https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1927725/full",
      "date": "2026-09-23",
      "written_by": "independent",
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  ],
  "replication": {
    "produced_by": [
      "Thirty-two gamified linguistic exercises administered in a browser",
      "A labeled sample of 3,644 children, 392 with professional diagnoses, gathered through 113 organizations",
      "A random-forest classifier trained on performance measures"
    ],
    "must_hold": [
      "Samples balanced on native language and other demographics, which the 2026 re-analysis finds this dataset is not",
      "Professional diagnosis as the reference standard",
      "Spanish-language content for Spanish-speaking populations"
    ],
    "echoes": [
      {
        "date": "2026-09-23",
        "setting": "Independent re-analysis of the same Dytective dataset (Vellore Institute of Technology)",
        "model_version": null,
        "outcome": "did not recur",
        "source": "Sudha M, Murali S (2026), Frontiers in Digital Health, https://europepmc.org/article/PMC/PMC13656794 (demographic columns alone reach AUC 0.724 against 0.744 for all task metrics)"
      }
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  "headline": "Dytective online dyslexia screener reaches 80.6% recall",
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    "status": "reversed",
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    "note": "An independent re-analysis of the same dataset reported that four demographic columns alone nearly match the 192 task metrics' discrimination and that including them yields a twelve-fold false-positive disparity between language groups, undercutting the screener's reported performance.",
    "quote": "A subgroup audit shows that dyslexia prevalence in this dataset differs 4.7-fold by native-language status, that four demographic columns alone reach AUC 0.724 against 0.744 for all 192 task metrics, and that including them yields a twelve-fold disparity in false-positive rate between language groups.",
    "url": "https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1927725/full"
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      "source": "https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1927725/full; https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0241687",
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      "change": "Benefit type set to Improved Task Accuracy under Trustible's full taxonomy of 23 types; it was Improved Accessibility among the six first used.",
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            "account": "Luz Rello (IE University, Madrid), Baeza-Yates, Ali, Bigham and Serra published in PLoS ONE (2 December 2020) a web-based gamified test of 32 linguistic exercises. Its performance measures feed a random-forest classifier with 200 trees. The study enrolled 3,644 participants aged 7–17, 392 with a professional dyslexia diagnosis, recruited through 113 organizations in Argentina, Chile, Colombia, Spain and the USA. They were 60 primary and secondary schools, 22 specialized dyslexia centers, 18 non-profits and 3 universities. The classifier reached 79.8% accuracy with 80.6% recall and 79.3% precision for the dyslexia class. A tablet validation with 1,395 participants aged 12–17 gave 78.1% recall and 76.2% precision. The authors state that an online Spanish screening tool based on their methods had already been used by more than 200,000 people. They disclose a patent application (US 15/493,060, Rello and Ballesteros). Sudha and Murali (Frontiers in Digital Health, 23 September 2026) re-analyzed the same 3,644-participant dataset. They report that dyslexia prevalence in the dataset differs 4.7-fold by native-language status. Four demographic columns alone reach AUC 0.724 against 0.744 for all 192 task metrics. Including them yields a twelve-fold disparity in false-positive rate between language groups.",
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                  "date": "2026-09-23",
                  "setting": "Independent re-analysis of the same Dytective dataset (Vellore Institute of Technology)",
                  "outcome": "did not recur",
                  "source": "Sudha M, Murali S (2026), Frontiers in Digital Health, https://europepmc.org/article/PMC/PMC13656794 (demographic columns alone reach AUC 0.724 against 0.744 for all task metrics)"
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