{
  "as_of": "2026-07-24",
  "guidelines": [
    {
      "conditions": {},
      "coverage_topics": [
        {
          "id": "design-and-randomization",
          "label": "Trial design, allocation, concealment, and masking"
        },
        {
          "id": "participants-and-flow",
          "label": "Eligibility, recruitment, analysis populations, and participant flow"
        },
        {
          "id": "outcomes-and-harms",
          "label": "Prespecified outcomes, estimates, uncertainty, and harms"
        },
        {
          "id": "registration-protocol-analysis-plan",
          "label": "Registration, protocol, analysis plan, and deviations"
        },
        {
          "id": "open-science-and-funding",
          "label": "Data sharing, funding, conflicts, and contributor information"
        }
      ],
      "id": "consort-2025",
      "name": "CONSORT 2025",
      "official_url": "https://www.consort-spirit.org/",
      "protocol": false,
      "role": "primary",
      "source_ids": [
        "SW-S07",
        "SW-S08"
      ],
      "study_designs": [
        "randomized_trial",
        "pilot_randomized_trial"
      ],
      "version": "2025"
    },
    {
      "conditions": {
        "ai": true
      },
      "coverage_topics": [
        {
          "id": "ai-intervention-description",
          "label": "AI intervention, inputs, outputs, version, and intended use"
        },
        {
          "id": "human-ai-interaction",
          "label": "Human oversight, user expertise, and interaction workflow"
        },
        {
          "id": "error-analysis",
          "label": "Performance errors, failure cases, and subgroup behavior"
        }
      ],
      "id": "consort-ai-2020",
      "name": "CONSORT-AI extension",
      "official_url": "https://www.equator-network.org/reporting-guidelines/consort-artificial-intelligence/",
      "protocol": false,
      "role": "extension",
      "source_ids": [
        "SW-S07",
        "SW-S31"
      ],
      "study_designs": [
        "randomized_trial",
        "pilot_randomized_trial"
      ],
      "version": "2020"
    },
    {
      "conditions": {},
      "coverage_topics": [
        {
          "id": "administrative-information",
          "label": "Registration, version, roles, funding, and governance"
        },
        {
          "id": "design-and-interventions",
          "label": "Trial design, interventions, allocation, and masking"
        },
        {
          "id": "outcomes-and-analysis",
          "label": "Outcomes, sample size, analysis plan, and missing data"
        },
        {
          "id": "data-monitoring-and-harms",
          "label": "Data management, monitoring, auditing, and harms"
        },
        {
          "id": "ethics-dissemination-and-access",
          "label": "Ethics, consent, confidentiality, dissemination, and access"
        }
      ],
      "id": "spirit-2025",
      "name": "SPIRIT 2025",
      "official_url": "https://www.consort-spirit.org/",
      "protocol": true,
      "role": "primary",
      "source_ids": [
        "SW-S07",
        "SW-S09"
      ],
      "study_designs": [
        "randomized_trial",
        "pilot_randomized_trial"
      ],
      "version": "2025"
    },
    {
      "conditions": {
        "ai": true
      },
      "coverage_topics": [
        {
          "id": "ai-intervention-versioning",
          "label": "AI intervention version, updates, inputs, outputs, and workflow"
        },
        {
          "id": "human-ai-protocol",
          "label": "Human oversight, user expertise, and interaction protocol"
        },
        {
          "id": "ai-error-monitoring",
          "label": "Error handling, performance monitoring, and safety"
        }
      ],
      "id": "spirit-ai-2020",
      "name": "SPIRIT-AI extension",
      "official_url": "https://www.equator-network.org/reporting-guidelines/spirit-artificial-intelligence/",
      "protocol": true,
      "role": "extension",
      "source_ids": [
        "SW-S09",
        "SW-S32"
      ],
      "study_designs": [
        "randomized_trial",
        "pilot_randomized_trial"
      ],
      "version": "2020"
    },
    {
      "conditions": {},
      "coverage_topics": [
        {
          "id": "eligibility-and-information-sources",
          "label": "Eligibility criteria, information sources, and complete search strategy"
        },
        {
          "id": "selection-and-data-collection",
          "label": "Selection, extraction, data items, and reviewer processes"
        },
        {
          "id": "bias-and-certainty",
          "label": "Risk of bias, reporting bias, and certainty assessment"
        },
        {
          "id": "synthesis-and-heterogeneity",
          "label": "Effect measures, synthesis methods, heterogeneity, and sensitivity analyses"
        },
        {
          "id": "registration-support-and-availability",
          "label": "Registration, protocol, amendments, support, conflicts, data, and code"
        }
      ],
      "id": "prisma-2020",
      "name": "PRISMA 2020",
      "official_url": "https://www.prisma-statement.org/prisma-2020",
      "protocol": false,
      "role": "primary",
      "source_ids": [
        "SW-S10"
      ],
      "study_designs": [
        "systematic_review",
        "meta_analysis"
      ],
      "version": "2020"
    },
    {
      "conditions": {},
      "coverage_topics": [
        {
          "id": "design-setting-and-participants",
          "label": "Design, setting, dates, eligibility, selection, and follow-up"
        },
        {
          "id": "variables-data-sources-and-bias",
          "label": "Variables, measurement, data sources, and bias"
        },
        {
          "id": "study-size-and-statistics",
          "label": "Study size, quantitative variables, confounding, missing data, and sensitivity"
        },
        {
          "id": "participants-outcomes-and-estimates",
          "label": "Participant flow, descriptive data, outcomes, and adjusted estimates"
        },
        {
          "id": "limitations-generalizability-and-funding",
          "label": "Limitations, interpretation, generalizability, and funding"
        }
      ],
      "id": "strobe-2007",
      "name": "STROBE",
      "official_url": "https://www.strobe-statement.org/checklists/",
      "protocol": false,
      "role": "primary",
      "source_ids": [
        "SW-S11"
      ],
      "study_designs": [
        "cohort",
        "case_control",
        "cross_sectional",
        "observational"
      ],
      "version": "2007"
    },
    {
      "conditions": {},
      "coverage_topics": [
        {
          "id": "participants-and-design",
          "label": "Participant selection, setting, design, and data collection"
        },
        {
          "id": "index-test-and-reference-standard",
          "label": "Index test, reference standard, thresholds, and blinding"
        },
        {
          "id": "analysis-and-indeterminate-results",
          "label": "Accuracy estimates, uncertainty, missing, and indeterminate results"
        },
        {
          "id": "flow-and-applicability",
          "label": "Participant flow, timing, adverse events, and applicability"
        }
      ],
      "id": "stard-2015",
      "name": "STARD 2015",
      "official_url": "https://resources.equator-network.org/reporting-guidelines/stard",
      "protocol": false,
      "role": "primary",
      "source_ids": [
        "SW-S12"
      ],
      "study_designs": [
        "diagnostic_accuracy"
      ],
      "version": "2015"
    },
    {
      "conditions": {
        "ai": true
      },
      "coverage_topics": [
        {
          "id": "dataset-and-model-provenance",
          "label": "Dataset provenance, model version, training status, and access"
        },
        {
          "id": "ai-test-conduct",
          "label": "AI index-test execution, human interaction, thresholds, and reference standard"
        },
        {
          "id": "fairness-failures-and-generalizability",
          "label": "Subgroups, fairness, failure cases, uncertainty, and generalizability"
        }
      ],
      "id": "stard-ai-2025",
      "name": "STARD-AI",
      "official_url": "https://www.nature.com/articles/s41591-025-03953-8",
      "protocol": false,
      "role": "extension",
      "source_ids": [
        "SW-S13"
      ],
      "study_designs": [
        "diagnostic_accuracy"
      ],
      "version": "2025"
    },
    {
      "conditions": {},
      "coverage_topics": [
        {
          "id": "data-source-and-participants",
          "label": "Data source, participants, setting, and outcome definition"
        },
        {
          "id": "predictors-and-sample-size",
          "label": "Predictors, sample size, missing data, and class balance"
        },
        {
          "id": "model-development-and-validation",
          "label": "Model development, tuning, validation, and updating"
        },
        {
          "id": "performance-and-clinical-utility",
          "label": "Performance, uncertainty, calibration, discrimination, and utility"
        },
        {
          "id": "fairness-transparency-and-access",
          "label": "Subgroups, fairness, interpretability, model access, data, and code"
        }
      ],
      "id": "tripod-ai-2024",
      "name": "TRIPOD+AI",
      "official_url": "https://www.tripod-statement.org/",
      "protocol": false,
      "role": "primary",
      "source_ids": [
        "SW-S14"
      ],
      "study_designs": [
        "prediction_model",
        "diagnostic_prediction_model",
        "prognostic_prediction_model"
      ],
      "version": "2024"
    },
    {
      "conditions": {
        "llm": true
      },
      "coverage_topics": [
        {
          "id": "llm-task-and-version",
          "label": "Task definition, model identity, version, prompts, and updates"
        },
        {
          "id": "evaluation-and-human-oversight",
          "label": "Evaluation design, human oversight, comparators, and failure analysis"
        },
        {
          "id": "llm-safety-and-reproducibility",
          "label": "Privacy, bias, safety, reproducibility, and access constraints"
        }
      ],
      "id": "tripod-llm-2025",
      "name": "TRIPOD-LLM",
      "official_url": "https://www.tripod-statement.org/",
      "protocol": false,
      "role": "extension",
      "source_ids": [
        "SW-S14"
      ],
      "study_designs": [
        "prediction_model",
        "diagnostic_prediction_model",
        "prognostic_prediction_model"
      ],
      "version": "2025"
    },
    {
      "conditions": {},
      "coverage_topics": [
        {
          "id": "patient-information-and-consent",
          "label": "De-identified patient information, consent, and perspective"
        },
        {
          "id": "timeline-and-clinical-findings",
          "label": "Timeline, clinical findings, diagnostic assessment, and reasoning"
        },
        {
          "id": "intervention-follow-up-and-outcomes",
          "label": "Interventions, follow-up, outcomes, adverse events, and adherence"
        },
        {
          "id": "discussion-and-limitations",
          "label": "Context, strengths, limitations, and cautious take-away lessons"
        }
      ],
      "id": "care-2013",
      "name": "CARE",
      "official_url": "https://www.care-statement.org/checklist",
      "protocol": false,
      "role": "primary",
      "source_ids": [
        "SW-S15"
      ],
      "study_designs": [
        "case_report"
      ],
      "version": "2013"
    },
    {
      "conditions": {},
      "coverage_topics": [
        {
          "id": "design-sample-size-and-criteria",
          "label": "Study design, experimental unit, sample size, and inclusion or exclusion"
        },
        {
          "id": "randomization-blinding-and-outcomes",
          "label": "Randomization, blinding, outcomes, and statistical methods"
        },
        {
          "id": "animals-procedures-and-welfare",
          "label": "Animal details, procedures, housing, welfare, and adverse events"
        },
        {
          "id": "ethics-interpretation-and-data",
          "label": "Ethics, limitations, interpretation, protocol, data, and conflicts"
        }
      ],
      "id": "arrive-2.0-2020",
      "name": "ARRIVE 2.0",
      "official_url": "https://arriveguidelines.org/arrive-guidelines",
      "protocol": false,
      "role": "primary",
      "source_ids": [
        "SW-S16"
      ],
      "study_designs": [
        "animal_in_vivo"
      ],
      "version": "2.0 (2020)"
    },
    {
      "conditions": {},
      "coverage_topics": [
        {
          "id": "problem-context-and-rationale",
          "label": "Local problem, available knowledge, context, rationale, and aims"
        },
        {
          "id": "intervention-study-and-measures",
          "label": "Intervention, study of the intervention, measures, and analysis"
        },
        {
          "id": "ethics-results-and-variation",
          "label": "Ethical considerations, evolution, outcomes, and variation over time"
        },
        {
          "id": "interpretation-limitations-and-sustainability",
          "label": "Interpretation, limitations, sustainability, spread, and funding"
        }
      ],
      "id": "squire-2.0-2015",
      "name": "SQUIRE 2.0",
      "official_url": "https://www.squire-statement.org/index.cfm?fuseaction=page.viewPage&pageID=471&nodeID=1",
      "protocol": false,
      "role": "primary",
      "source_ids": [
        "SW-S17"
      ],
      "study_designs": [
        "quality_improvement"
      ],
      "version": "2.0 (2015)"
    },
    {
      "conditions": {},
      "coverage_topics": [
        {
          "id": "population-setting-perspective",
          "label": "Population, setting, perspective, comparators, and time horizon"
        },
        {
          "id": "effects-costs-and-currency",
          "label": "Health outcomes, resources, costs, currency, and price date"
        },
        {
          "id": "model-assumptions-and-uncertainty",
          "label": "Model, assumptions, analysis plan, uncertainty, and distributional effects"
        },
        {
          "id": "engagement-results-and-conflicts",
          "label": "Stakeholder engagement, results, limitations, funding, and conflicts"
        }
      ],
      "id": "cheers-2022",
      "name": "CHEERS 2022",
      "official_url": "https://www.ispor.org/heor-resources/good-practices/article/consolidated-health-economic-evaluation-reporting-standards-2022-cheers-2022-statement-updated-reporting-guidance-for-health-economic-evaluations",
      "protocol": false,
      "role": "primary",
      "source_ids": [
        "SW-S18"
      ],
      "study_designs": [
        "health_economic_evaluation"
      ],
      "version": "2022"
    },
    {
      "conditions": {},
      "coverage_topics": [
        {
          "id": "approach-context-and-researchers",
          "label": "Qualitative approach, context, researcher characteristics, and reflexivity"
        },
        {
          "id": "sampling-ethics-and-data-collection",
          "label": "Sampling, ethics, consent, data collection, and instruments"
        },
        {
          "id": "analysis-trustworthiness-and-findings",
          "label": "Analysis, trustworthiness, findings, quotations, and links to data"
        },
        {
          "id": "limitations-implications-and-funding",
          "label": "Limitations, implications, conflicts, and funding"
        }
      ],
      "id": "srqr-2014",
      "name": "SRQR",
      "official_url": "https://www.equator-network.org/reporting-guidelines/srqr/",
      "protocol": false,
      "role": "primary",
      "source_ids": [
        "SW-S33"
      ],
      "study_designs": [
        "qualitative"
      ],
      "version": "2014"
    }
  ],
  "schema_version": "1.0"
}