[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100657171":3},{"organization":4,"outcomesModule":7,"designInfo":36,"detailedDescription":44,"studyPopulation":35,"armGroups":45,"interventions":57,"overallOfficials":66,"centralContacts":71,"locations":76,"responsibleParty":92,"collaborators":35,"id":94,"slug":95,"hasResults":96,"nctId":97,"briefTitle":98,"officialTitle":99,"acronym":100,"eligibilityCriteria":101,"healthyVolunteers":102,"sex":103,"minAge":104,"maxAge":35,"enrollmentInfo":105,"targetDuration":35,"studyType":108,"phases":109,"briefSummary":111,"conditions":112,"keywords":114,"overallStatus":121,"whyStopped":35,"lastUpdateSubmitDate":122,"lastUpdatePostDateStruct":123,"startDateStruct":126,"completionDateStruct":128,"leadSponsor":130,"locationsCount":131},{"fullName":5,"class":6},"Shanghai Zhongshan Hospital","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":35},[9],{"measure":10,"description":11,"timeFrame":12},"Difference in Absolute Anaerobic Threshold Localization Error","Absolute difference, in seconds, between the physician-selected AT time and the prespecified expert reference AT time. The primary estimand is the within-physician difference between AI-assisted and unassisted interpretations, analyzed across the same examinations using a crossed mixed-effects model.","Recorded immediately after each case interpretation and analyzed after completion of both study periods, up to 2 months",[14,17,20,23,26,29,32],{"measure":15,"description":16,"timeFrame":12},"Signed Anaerobic Threshold Localization Error","Signed difference, in seconds, between the physician-selected AT time and the prespecified expert reference AT time. Negative values indicate earlier placement and positive values indicate later placement relative to the reference.",{"measure":18,"description":19,"timeFrame":12},"Proportion of Anaerobic Threshold Estimates Within 15 Seconds of the Reference","Proportion of interpretable examinations for which the physician-selected AT time is within 15 seconds, in either direction, of the prespecified expert reference AT time.",{"measure":21,"description":22,"timeFrame":12},"Proportion of Anaerobic Threshold Estimates Within 30 Seconds of the Reference","Proportion of interpretable examinations for which the physician-selected AT time is within 30 seconds, in either direction, of the prespecified expert reference AT time.",{"measure":24,"description":25,"timeFrame":12},"Agreement With the Reference Anaerobic Threshold Status","Agreement between the physician classification (AT confirmed, AT not reached, or unable to confirm) and the prespecified expert reference classification. Results will be summarized as overall agreement and condition-specific classification performance.",{"measure":27,"description":28,"timeFrame":12},"Inter-Reader Reliability","Reliability among physicians for AT timing and AT-status classification under AI-assisted and unassisted conditions, summarized using appropriate intraclass correlation and chance-corrected agreement statistics with 95% confidence intervals.",{"measure":30,"description":31,"timeFrame":12},"Interpretation Time per Examination","Elapsed time, in seconds, from opening the blinded CPET task to submission of the physician's AT classification and, when applicable, the selected AT time.",{"measure":33,"description":34,"timeFrame":12},"Interaction Between AI Assistance and Physician Seniority","Difference in the effect of AI assistance between senior and junior physicians, estimated from the prespecified assistance-by-seniority interaction term in the crossed mixed-effects model for absolute AT localization error.",null,{"allocation":37,"interventionModel":38,"interventionModelDescription":39,"primaryPurpose":40,"observationalModel":35,"timePerspective":35,"maskingInfo":41},"RANDOMIZED","CROSSOVER","Randomized, stratified, two-period crossover reader study. Sixteen physicians (8 senior and 8 junior) are randomized within seniority strata to two sequences. The 150 de-identified CPET examinations are divided into libraries X and Y (75 each). In period 1, Sequence A interprets X without AI and Y with AI, whereas Sequence B receives the opposite conditions. After a 14 ± 3 day washout, conditions are reversed in period 2. Each physician interprets every case once without AI and once with AI, yielding 4,800 interpretations.","DIAGNOSTIC",{"masking":42,"maskingDescription":43,"whoMasked":35},"NONE","Physician participants cannot be masked to whether the AI prediction is displayed. They are masked to case source, routine-report and reference-standard labels, other physicians' annotations, and their own prior-period annotations. Case order is randomized and new blinded task identifiers are used in period 2.","The 150 de-identified CPET examinations will be divided into two non-overlapping case libraries, X and Y, with 75 examinations in each library. The libraries will be balanced, as far as feasible, by clinical severity and acquisition characteristics. None of the reader-study examinations will be used for model training, hyperparameter selection, or checkpoint selection.\n\nWithin each seniority stratum, physicians will be randomized in a 1:1 ratio to Sequence A or Sequence B. In period 1, Sequence A will interpret library X without AI assistance and library Y with AI assistance; Sequence B will receive the opposite conditions. Following a 14 ± 3 day washout, the assistance conditions will be reversed in period 2. Case order will be randomized within each period, and new blinded task identifiers will be assigned in period 2. Thus, every physician will interpret every examination once without AI assistance and once with AI assistance, yielding 4,800 interpretations.\n\nIn the assisted condition, the interface will display the frozen model's predicted AT time. In the unassisted condition, no model output will be shown. Readers will be masked to case source, routine-report and reference-standard labels, other readers' annotations, and their own annotations from the previous period. For each examination, the reader will classify AT as confirmed, not reached, or unable to confirm. When AT is confirmed, the reader will record the AT time and the interpretive method used. Interpretation time will also be recorded.\n\nThe reference standard will be prespecified from unassisted senior-physician annotations. AT will be considered present when at least 6 of 8 senior physicians confirm it, and the reference AT time will be the median of the confirming senior estimates. AT will be considered not reached when at least 6 of 8 senior physicians make that classification. Discordant cases will undergo adjudication by an additional senior physician who is masked to model output and reader identity. For analyses of senior readers, a leave-one-reader-out reference will be used.\n\nThe primary analysis will compare absolute AT localization error between assisted and unassisted interpretations using a crossed mixed-effects model. Fixed effects will include assistance condition, physician seniority, the assistance-by-seniority interaction, period, randomized sequence, and case library. Random intercepts will be included for physician and examination. Estimates will be reported with 95% confidence intervals. No simulated or pilot-result data will be included in the prospective study analysis.",[46,53],{"label":47,"type":48,"description":49,"interventionNames":50},"Sequence A","EXPERIMENTAL","Period 1: library X interpreted without AI assistance and library Y interpreted with AI assistance. Period 2, after a 14 ± 3 day washout: library X interpreted with AI assistance and library Y interpreted without AI assistance.",[51,52],"Other: AI-Assisted Anaerobic Threshold Interpretation","Other: Unassisted Cardiopulmonary Exercise Test Interpretation",{"label":54,"type":48,"description":55,"interventionNames":56},"Sequence B","Period 1: library X interpreted with AI assistance and library Y interpreted without AI assistance. Period 2, after a 14 ± 3 day washout: library X interpreted without AI assistance and library Y interpreted with AI assistance.",[51,52],[58,62],{"type":6,"name":59,"description":60,"armGroupLabels":61,"otherNames":35},"AI-Assisted Anaerobic Threshold Interpretation","A frozen PACE-Former model provides a predicted anaerobic threshold time within the CPET review interface. Physicians retain responsibility for the final classification and AT time annotation. No other model output or reference label is displayed.",[47,54],{"type":6,"name":63,"description":64,"armGroupLabels":65,"otherNames":35},"Unassisted Cardiopulmonary Exercise Test Interpretation","The physician reviews the same de-identified CPET data using the study interface without display of any model prediction or AI-generated suggestion and independently classifies AT status and records the AT time when confirmed.",[47,54],[67],{"name":68,"affiliation":69,"role":70},"Cong Wang, MD, PhD","Fudan University","PRINCIPAL_INVESTIGATOR",[72],{"name":68,"role":73,"phone":74,"phoneExt":35,"email":75},"CONTACT","+86-21-64041990","wang.cong1@zs-hospital.sh.cn",[77],{"facility":78,"status":35,"city":79,"state":80,"zip":81,"country":82,"countryCode":83,"cosmosGeoPoint":84,"geoPoint":89,"contacts":90},"Zhongshan Hospital, Fudan University","Shanghai","Shanghai Municipality","200032","China","CN",{"type":85,"coordinates":86},"Point",[87,88],121.45806,31.22222,{"lat":88,"lon":87},[91],{"name":68,"role":73,"phone":74,"phoneExt":35,"email":75},{"type":93,"investigatorFullName":35,"investigatorTitle":35,"investigatorAffiliation":35,"oldNameTitle":35,"oldOrganization":35},"SPONSOR","100657171","ai-assisted-anaerobic-threshold-interpretation-in-cardiopulmonary-exercise-testing-100657171",false,"NCT07833137","AI-Assisted Anaerobic Threshold Interpretation in Cardiopulmonary Exercise Testing","A Randomized Two-Period Crossover Reader Study of Artificial Intelligence-Assisted Anaerobic Threshold Interpretation in Cardiopulmonary Exercise Testing","PACE-Reader","Inclusion Criteria:\n\n* Age 18 years or older.\n* Licensed physician or physician enrolled in a recognized postgraduate clinical training program at the study institution.\n* Eligible for one of the two prespecified seniority strata: senior physicians are attending physicians or above with at least 5 years of independent CPET interpretation experience; junior physicians are residents or specialty trainees with less than 5 years of independent CPET interpretation experience.\n* Able to interpret CPET examinations and use the study interface after standardized orientation.\n* Willing and able to complete both study periods and the prespecified washout interval.\n* Provides written informed consent.\n\nExclusion Criteria:\n\n* Direct involvement in development, training, hyperparameter selection, or checkpoint selection of the PACE-Former model.\n* Prior access to the study case libraries, reference-standard annotations, or model outputs for the study examinations.\n* Unable to complete the required interpretation tasks or the 14 ± 3 day washout interval.\n* Withdrawal of consent before completion of study procedures.",true,"ALL","18 Years",{"count":106,"type":107},16,"ESTIMATED","INTERVENTIONAL",[110],"NA","This prospective randomized two-period crossover reader study will evaluate whether assistance from a frozen artificial intelligence model improves physicians' interpretation of the anaerobic threshold (AT) in cardiopulmonary exercise testing (CPET). Sixteen physicians, including 8 senior and 8 junior readers, will each interpret 150 de-identified CPET examinations once without AI assistance and once with AI assistance. The primary outcome is the within-physician difference in absolute AT localization error between the assisted and unassisted conditions. Secondary outcomes include signed timing error, agreement on whether AT is present, estimates within 15 and 30 seconds of the reference, inter-reader reliability, interpretation time, and whether the effect of AI assistance differs by physician seniority.",[113],"Cardiopulmonary Exercise Test Interpretation",[115,116,117,118,119,120],"Artificial Intelligence","Anaerobic Threshold","Cardiopulmonary Exercise Testing","Clinical Decision Support","Reader Study","Human-AI Collaboration","NOT_YET_RECRUITING","2026-09-16",{"date":124,"type":125},"2026-09-22","ACTUAL",{"date":127,"type":107},"2026-09-25",{"date":129,"type":107},"2026-11-25",{"name":5,"class":6},1]