Raw JSON
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'timeFrame': '3 months', 'description': 'Wireless elbow-mounted accelerometer and gyroscopic sensors worn by the participant will generate 6 degree of freedom motion data which will be analysed using validated, bespoke algorithms. 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These data will be analysed for the relative activity and dominance of each hand during the procedure while participants perform a diagnostic arthroscopy of the knee in theatre according to a standardised protocol.', 'reportingStatus': 'NOT_POSTED', 'denomUnitsSelected': 'Participants'}, {'type': 'SECONDARY', 'title': 'Global Rating Scale Performance During Diagnostic Knee Arthroscopy in Theatre', 'denoms': [{'units': 'Participants', 'counts': [{'value': '0', 'groupId': 'OG000'}, {'value': '0', 'groupId': 'OG001'}]}], 'groups': [{'id': 'OG000', 'title': 'Simulation Training', 'description': 'Addition of simulation training during usual clinical training as part of a GMC (General Medical Council) recognised Deanery training programme\n\nSimulation training: Simulation training in a skills lab for 1 hour per week over 13 weeks on dry, bench-top box trainers and anatomical simulators'}, {'id': 'OG001', 'title': 'Non-simulation/Routine Training', 'description': 'Usual clinical training as 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Training', 'description': 'Usual clinical training as part of a GMC (General Medical Council) recognised Deanery training programme'}], 'classes': [{'title': 'Ratio of Hand movements', 'categories': [{'measurements': [{'value': '1.9', 'groupId': 'OG000', 'lowerLimit': '1.5', 'upperLimit': '2.1'}, {'value': '3.3', 'groupId': 'OG001', 'lowerLimit': '2.2', 'upperLimit': '4.8'}]}]}, {'title': 'Ratio of Minor hand movements', 'categories': [{'measurements': [{'value': '3.8', 'groupId': 'OG000', 'lowerLimit': '2.8', 'upperLimit': '4.7'}, {'value': '10.3', 'groupId': 'OG001', 'lowerLimit': '5.1', 'upperLimit': '11.9'}]}]}, {'title': 'Ratio of Smoothness', 'categories': [{'measurements': [{'value': '1.2', 'groupId': 'OG000', 'lowerLimit': '1.1', 'upperLimit': '1.7'}, {'value': '2.6', 'groupId': 'OG001', 'lowerLimit': '1.6', 'upperLimit': '3.0'}]}]}, {'title': 'Ratio of Time taken', 'categories': [{'measurements': [{'value': '2.1', 'groupId': 'OG000', 'lowerLimit': '1.8', 'upperLimit': '2.8'}, {'value': '4.3', 'groupId': 'OG001', 'lowerLimit': '2.8', 'upperLimit': '5.4'}]}]}], 'paramType': 'MEDIAN', 'timeFrame': '3 months', 'description': "Previously described motion parameters of participants performing a diagnostic knee arthroscopy in theatre (see Primary outcome 1, and secondary outcomes 2-8) reported as a ratio to the 'ideal' performance as measured from the supervising clinician performing an optimal diagnostic knee arthroscopy on the same patient as the participant while wearing the wireless elbow-mounted accelerometer and gyroscopic sensors which will record 6 degree of freedom motion data to allow calculation of 'number of hand movements', 'smoothness', 'time taken', 'minor hand movements', 'stationary time', 'idle time' and dominance'", 'unitOfMeasure': 'Performance ratio (Participant:superviso', 'dispersionType': 'Inter-Quartile Range', 'reportingStatus': 'POSTED'}, {'type': 'SECONDARY', 'title': 'Motion Analysis Parameters During Simulation', 'denoms': [{'units': 'Participants', 'counts': [{'value': '15', 'groupId': 'OG000'}, {'value': '15', 'groupId': 'OG001'}]}], 'groups': [{'id': 'OG000', 'title': 'Simulation Training', 'description': 'Addition of simulation training during usual clinical training as part of a GMC (General Medical Council) recognised Deanery training programme\n\nSimulation training: Simulation training in a skills lab for 1 hour per week over 13 weeks on dry, bench-top box trainers and anatomical simulators'}, {'id': 'OG001', 'title': 'Non-simulation/Routine Training', 'description': 'Usual clinical training as part of a GMC (General Medical Council) recognised Deanery training programme'}], 'classes': [{'categories': [{'measurements': [{'value': '131', 'groupId': 'OG000', 'lowerLimit': '108', 'upperLimit': '153'}, {'value': '249', 'groupId': 'OG001', 'lowerLimit': '197', 'upperLimit': '345'}]}]}], 'paramType': 'MEDIAN', 'timeFrame': '3 months', 'description': 'Change in participant performance on dry, bench top box trainers and anatomical simulators between baseline and 3 months using motion analysis parameters described in Primary outcome 1 and secondary outcomes 2-8 as measured by wireless elbow-mounted accelerometer and gyroscopic sensors', 'unitOfMeasure': 'Hand movements', 'dispersionType': 'Inter-Quartile Range', 'reportingStatus': 'POSTED'}, {'type': 'SECONDARY', 'title': 'Resting State Network Functional Changes on fMRI (Functional Magnetic Resonance Imaging)', 'timeFrame': '3 months', 'description': 'Use of MELODIC (Multivariate Exploratory Linear Optimized Decomposition into Independent Components) to identify resting state networks, and analyse differences in functional connectivity at baseline and three months between the intervention and control arms.', 'reportingStatus': 'NOT_POSTED', 'denomUnitsSelected': 'Participants'}, {'type': 'SECONDARY', 'title': 'Voxel Based Morphometry Structural Changes on fMRI (Functional Magnetic Resonance Imaging)', 'timeFrame': '3 months', 'description': "Using FSLVBM (fMRIB's Software Library Voxel Based Morphometry) to calculate voxel-wise changes in grey matter volumes at baseline and three months between the intervention and control arms. Changes in VBM imply changes in grey matter volume and represent structural brain change.", 'reportingStatus': 'NOT_POSTED', 'denomUnitsSelected': 'Participants'}, {'type': 'SECONDARY', 'title': 'Diffusion Tractography Structural Changes on fMRI (Functional Magnetic Resonance Imaging)', 'timeFrame': '3 months', 'description': "Using FDT (fMRIB's Diffusion Toolbox) to model local diffusion and changes in tractography at baseline and three months between the intervention and control arms. Changes in diffusion imply micro-structural (axonal) connectivity and represent structural brain change.", 'reportingStatus': 'NOT_POSTED', 'denomUnitsSelected': 'Participants'}, {'type': 'SECONDARY', 'title': 'Quantitative Magnetisation Transfer Structural Changes on fMRI (Functional Magnetic Resonance Imaging)', 'timeFrame': '3 months', 'description': 'Quantitative magnetisation transfer imaging estimates liquid and semisolid (macromolecular) constituents of tissue at baseline and three months between the intervention and control arms. Changes in macromolecular content imply micro-structural (myelin) connectivity and represent structural brain change.', 'reportingStatus': 'NOT_POSTED', 'denomUnitsSelected': 'Participants'}, {'type': 'SECONDARY', 'title': 'Feasibility of Additional Simulation Training', 'denoms': [{'units': 'Participants', 'counts': [{'value': '0', 'groupId': 'OG000'}, {'value': '0', 'groupId': 'OG001'}]}], 'groups': [{'id': 'OG000', 'title': 'Simulation Training', 'description': 'Addition of simulation training during usual clinical training as part of a GMC (General Medical Council) recognised Deanery training programme\n\nSimulation training: Simulation training in a skills lab for 1 hour per week over 13 weeks on dry, bench-top box trainers and anatomical simulators'}, {'id': 'OG001', 'title': 'Non-simulation/Routine Training', 'description': 'Usual clinical training as part of a GMC (General Medical Council) recognised Deanery training programme'}], 'timeFrame': '3 months', 'description': 'Qualitative survey of participants opinions of the addition of simulation to their usual clinical training programme', 'reportingStatus': 'POSTED', 'populationDescription': 'Data were not collected'}]}, 'participantFlowModule': {'groups': [{'id': 'FG000', 'title': 'Simulation Training', 'description': 'Addition of simulation training during usual clinical training as part of a GMC (General Medical Council) recognised Deanery training programme\n\nSimulation training: Simulation training in a skills lab for 1 hour per week over 13 weeks on dry, bench-top box trainers and anatomical simulators'}, {'id': 'FG001', 'title': 'Non-simulation/Routine Training', 'description': 'Usual clinical training as part of a GMC (General Medical Council) recognised Deanery training programme'}], 'periods': [{'title': 'Overall Study', 'milestones': [{'type': 'STARTED', 'achievements': [{'groupId': 'FG000', 'numSubjects': '15'}, {'groupId': 'FG001', 'numSubjects': '15'}]}, {'type': 'COMPLETED', 'achievements': [{'groupId': 'FG000', 'numSubjects': '15'}, {'groupId': 'FG001', 'numSubjects': '13'}]}, {'type': 'NOT COMPLETED', 'achievements': [{'groupId': 'FG000', 'numSubjects': '0'}, {'groupId': 'FG001', 'numSubjects': '2'}]}], 'dropWithdraws': [{'type': 'Withdrawal by Subject', 'reasons': [{'groupId': 'FG000', 'numSubjects': '0'}, {'groupId': 'FG001', 'numSubjects': '1'}]}, {'type': 'Lost to Follow-up', 'reasons': [{'groupId': 'FG000', 'numSubjects': '0'}, {'groupId': 'FG001', 'numSubjects': '1'}]}]}], 'recruitmentDetails': 'SHO trainees (PGY 2-3 equivalent) within nationally approved T\\&O training rotations at an English teaching hospital were eligible for inclusion. Exclusions; more than 2 years of surgical training; previous admission to a higher surgical training program; performed or assisted in over 10 arthroscopic or minimal-access procedures.'}, 'baselineCharacteristicsModule': {'denoms': [{'units': 'Participants', 'counts': [{'value': '15', 'groupId': 'BG000'}, {'value': '15', 'groupId': 'BG001'}, {'value': '30', 'groupId': 'BG002'}]}], 'groups': [{'id': 'BG000', 'title': 'Simulation Training', 'description': 'Addition of simulation training during usual clinical training as part of a GMC (General Medical Council) recognised Deanery training programme Simulation training: Simulation training in a skills lab for 1 hour per week over 13 weeks on dry, bench-top box trainers and anatomical simulators'}, {'id': 'BG001', 'title': 'Non-simulation/Routine Training', 'description': 'Usual clinical training as part of a GMC (General Medical Council) recognised Deanery training programme'}, {'id': 'BG002', 'title': 'Total', 'description': 'Total of all reporting groups'}], 'measures': [{'title': 'Age, Continuous', 'classes': [{'denoms': [{'units': 'Participants', 'counts': [{'value': '15', 'groupId': 'BG000'}, {'value': '15', 'groupId': 'BG001'}, {'value': '30', 'groupId': 'BG002'}]}], 'categories': [{'measurements': [{'value': '26.02', 'groupId': 'BG000', 'lowerLimit': '25.76', 'upperLimit': '27.18'}, {'value': '26.34', 'groupId': 'BG001', 'lowerLimit': '26.09', 'upperLimit': '27.17'}, {'value': '26.18', 'groupId': 'BG002', 'lowerLimit': '25.76', 'upperLimit': '27.18'}]}]}], 'paramType': 'MEAN', 'unitOfMeasure': 'years', 'dispersionType': 'INTER_QUARTILE_RANGE'}, {'title': 'Sex: Female, Male', 'classes': [{'denoms': [{'units': 'Participants', 'counts': [{'value': '15', 'groupId': 'BG000'}, {'value': '15', 'groupId': 'BG001'}, {'value': '30', 'groupId': 'BG002'}]}], 'categories': [{'title': 'Female', 'measurements': [{'value': '8', 'groupId': 'BG000'}, {'value': '6', 'groupId': 'BG001'}, {'value': '14', 'groupId': 'BG002'}]}, {'title': 'Male', 'measurements': [{'value': '7', 'groupId': 'BG000'}, {'value': '9', 'groupId': 'BG001'}, {'value': '16', 'groupId': 'BG002'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'unitOfMeasure': 'Participants'}, {'title': 'Race and Ethnicity Not Collected', 'classes': [{'denoms': [{'units': 'Participants', 'counts': [{'value': '0', 'groupId': 'BG000'}, {'value': '0', 'groupId': 'BG001'}, {'value': '0', 'groupId': 'BG002'}]}], 'categories': [{'measurements': [{'value': '0', 'groupId': 'BG002'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'unitOfMeasure': 'Participants', 'populationDescription': 'Race and Ethnicity were not collected from any participant.'}, {'title': 'Postgraduate year', 'classes': [{'title': 'PGY2', 'denoms': [{'units': 'Participants', 'counts': [{'value': '15', 'groupId': 'BG000'}, {'value': '15', 'groupId': 'BG001'}, {'value': '30', 'groupId': 'BG002'}]}], 'categories': [{'measurements': [{'value': '12', 'groupId': 'BG000'}, {'value': '12', 'groupId': 'BG001'}, {'value': '24', 'groupId': 'BG002'}]}]}, {'title': 'PGY3', 'denoms': [{'units': 'Participants', 'counts': [{'value': '15', 'groupId': 'BG000'}, {'value': '15', 'groupId': 'BG001'}, {'value': '30', 'groupId': 'BG002'}]}], 'categories': [{'measurements': [{'value': '3', 'groupId': 'BG000'}, {'value': '3', 'groupId': 'BG001'}, {'value': '6', 'groupId': 'BG002'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'unitOfMeasure': 'Participants'}]}}, 'documentSection': {'largeDocumentModule': {'largeDocs': [{'date': '2015-11-16', 'size': 219416, 'label': 'Study Protocol and Statistical Analysis Plan', 'hasIcf': False, 'hasSap': True, 'filename': 'Prot_SAP_000.pdf', 'typeAbbrev': 'Prot_SAP', 'uploadDate': '2020-11-20T05:05', 'hasProtocol': True}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'SINGLE', 'whoMasked': ['OUTCOMES_ASSESSOR']}, 'primaryPurpose': 'OTHER', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 30}}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2016-01'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2016-05', 'completionDateStruct': {'date': '2017-07', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2021-05-25', 'studyFirstSubmitDate': '2016-05-10', 'resultsFirstSubmitDate': '2019-04-23', 'studyFirstSubmitQcDate': '2016-05-16', 'lastUpdatePostDateStruct': {'date': '2021-06-18', 'type': 'ACTUAL'}, 'resultsFirstSubmitQcDate': '2021-05-25', 'studyFirstPostDateStruct': {'date': '2016-05-19', 'type': 'ESTIMATED'}, 'resultsFirstPostDateStruct': {'date': '2021-06-18', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2017-07', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Number of Hand Movements Required by Participants to Perform a Diagnostic Arthroscopy of the Knee in Theatre', 'timeFrame': '3 months', 'description': "Wireless elbow-mounted accelerometer and gyroscopic sensors worn by the participant will generate 6 degree of freedom motion data (three rotational degrees around the x, y and z axes, known as 'roll', 'pitch', and 'yaw', and three translational degrees of freedom along x, y and z axes, known as 'surge', 'sway' and 'heave') which will be analysed using validated, bespoke algorithms to calculate the number of hand movements taken whilst performing a diagnostic knee arthroscopy according to a standardised protocol."}], 'secondaryOutcomes': [{'measure': 'Smoothness of Hand Movements by Participants to Perform a Diagnostic Arthroscopy of the Knee in Theatre', 'timeFrame': '3 months', 'description': "Wireless elbow-mounted accelerometer and gyroscopic sensors worn by the participant will generate 6 degree of freedom motion data which will be analysed using validated, bespoke algorithms to calculate the smoothness (also known as 'jerk', the first derivative of acceleration by time, or third derivative of distance by time) of hand movements taken whilst performing a diagnostic knee arthroscopy according to a standardised protocol according to a standardised protocol."}, {'measure': 'Time Taken by Participants to Perform a Diagnostic Arthroscopy of the Knee in Theatre', 'timeFrame': '3 months', 'description': 'Wireless elbow-mounted accelerometer and gyroscopic sensors worn by the participant will generate 6 degree of freedom motion data which will be analysed using validated, bespoke algorithms. These data will also collect time signatures, which can be used to work out the time taken by participants to perform a diagnostic arthroscopy of the knee in theatre according to a standardised protocol.'}, {'measure': 'Minor Hand Movements Required by Participants to Perform a Diagnostic Arthroscopy of the Knee in Theatre', 'timeFrame': '3 months', 'description': "Wireless elbow-mounted accelerometer and gyroscopic sensors worn by the participant will generate 6 degree of freedom motion data (three rotational degrees around the x, y and z axes, known as 'roll', 'pitch', and 'yaw', and three translational degrees of freedom along x, y and z axes, known as 'surge', 'sway' and 'heave') which will be analysed using validated, bespoke algorithms to calculate the number of movements (below the threshold for 'hand movements' above in outcome 1, but above the data noise threshold) taken whilst performing a diagnostic knee arthroscopy according to a standardised protocol."}, {'measure': 'Stationary Time of Participants to Perform a Diagnostic Arthroscopy of the Knee in Theatre', 'timeFrame': '3 months', 'description': 'Wireless elbow-mounted accelerometer and gyroscopic sensors worn by the participant will generate 6 degree of freedom motion data which will be analysed using validated, bespoke algorithms. These data will also collect time signatures, which can be used to work out the length of time during the procedure where each hand is stationary while participants perform a diagnostic arthroscopy of the knee in theatre according to a standardised protocol.'}, {'measure': 'Idle Time of Participants to Perform a Diagnostic Arthroscopy of the Knee in Theatre', 'timeFrame': '3 months', 'description': 'Wireless elbow-mounted accelerometer and gyroscopic sensors worn by the participant will generate 6 degree of freedom motion data which will be analysed using validated, bespoke algorithms. These data will also collect time signatures, which can be used to work out the length of time during the procedure where both hands are stationary at the same time while participants perform a diagnostic arthroscopy of the knee in theatre according to a standardised protocol.'}, {'measure': 'Dominance of Participants to Perform a Diagnostic Arthroscopy of the Knee in Theatre', 'timeFrame': '3 months', 'description': 'Wireless elbow-mounted accelerometer and gyroscopic sensors worn by the participant will generate 6 degree of freedom motion data which will be analysed using validated, bespoke algorithms. These data will be analysed for the relative activity and dominance of each hand during the procedure while participants perform a diagnostic arthroscopy of the knee in theatre according to a standardised protocol.'}, {'measure': 'Global Rating Scale Performance During Diagnostic Knee Arthroscopy in Theatre', 'timeFrame': '3 months', 'description': 'Validated global rating scale for assessing diagnostic knee arthroscopy performance'}, {'measure': "Deviation From 'Idealised' Motion Parameters for Participants to Perform a Diagnostic Arthroscopy of the Knee in Theatre", 'timeFrame': '3 months', 'description': "Previously described motion parameters of participants performing a diagnostic knee arthroscopy in theatre (see Primary outcome 1, and secondary outcomes 2-8) reported as a ratio to the 'ideal' performance as measured from the supervising clinician performing an optimal diagnostic knee arthroscopy on the same patient as the participant while wearing the wireless elbow-mounted accelerometer and gyroscopic sensors which will record 6 degree of freedom motion data to allow calculation of 'number of hand movements', 'smoothness', 'time taken', 'minor hand movements', 'stationary time', 'idle time' and dominance'"}, {'measure': 'Motion Analysis Parameters During Simulation', 'timeFrame': '3 months', 'description': 'Change in participant performance on dry, bench top box trainers and anatomical simulators between baseline and 3 months using motion analysis parameters described in Primary outcome 1 and secondary outcomes 2-8 as measured by wireless elbow-mounted accelerometer and gyroscopic sensors'}, {'measure': 'Resting State Network Functional Changes on fMRI (Functional Magnetic Resonance Imaging)', 'timeFrame': '3 months', 'description': 'Use of MELODIC (Multivariate Exploratory Linear Optimized Decomposition into Independent Components) to identify resting state networks, and analyse differences in functional connectivity at baseline and three months between the intervention and control arms.'}, {'measure': 'Voxel Based Morphometry Structural Changes on fMRI (Functional Magnetic Resonance Imaging)', 'timeFrame': '3 months', 'description': "Using FSLVBM (fMRIB's Software Library Voxel Based Morphometry) to calculate voxel-wise changes in grey matter volumes at baseline and three months between the intervention and control arms. Changes in VBM imply changes in grey matter volume and represent structural brain change."}, {'measure': 'Diffusion Tractography Structural Changes on fMRI (Functional Magnetic Resonance Imaging)', 'timeFrame': '3 months', 'description': "Using FDT (fMRIB's Diffusion Toolbox) to model local diffusion and changes in tractography at baseline and three months between the intervention and control arms. Changes in diffusion imply micro-structural (axonal) connectivity and represent structural brain change."}, {'measure': 'Quantitative Magnetisation Transfer Structural Changes on fMRI (Functional Magnetic Resonance Imaging)', 'timeFrame': '3 months', 'description': 'Quantitative magnetisation transfer imaging estimates liquid and semisolid (macromolecular) constituents of tissue at baseline and three months between the intervention and control arms. Changes in macromolecular content imply micro-structural (myelin) connectivity and represent structural brain change.'}, {'measure': 'Feasibility of Additional Simulation Training', 'timeFrame': '3 months', 'description': 'Qualitative survey of participants opinions of the addition of simulation to their usual clinical training programme'}]}, 'oversightModule': {'oversightHasDmc': False}, 'conditionsModule': {'keywords': ['Simulation', 'Arthroscopy', 'Motion analysis'], 'conditions': ['Arthroscopy Training']}, 'descriptionModule': {'briefSummary': "The purpose of this study is to determine whether simulation training improves the performance during arthroscopic surgery ('keyhole' surgery into a joint).", 'detailedDescription': 'This single blinded randomised controlled study of junior orthopaedic trainees aims to assess whether the addition of simulation training improves arthroscopic technical skills performance of junior orthopaedic trainees during knee arthroscopy in the operating theatre compared to their usual clinical training programme. This will be assessed using objective motion analysis parameters recorded from wireless elbow-mounted motion sensors during surgery.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Participant is willing and able to give informed consent for participation in the study.\n* Healthy adults, Male or Female, aged 18 years or above.\n* Enrolled in Health Education Thames Valley/Oxford Deanery Training Programme in junior surgical training posts\n\nExclusion Criteria:\n\n* Unwilling or unable to provide informed consent\n* Previously completed higher surgical training programme'}, 'identificationModule': {'nctId': 'NCT02777333', 'briefTitle': 'Simulation-based Arthroscopic Surgery Study', 'organization': {'class': 'OTHER', 'fullName': 'University of Oxford'}, 'officialTitle': 'Simulation-based Arthroscopic Surgery Study', 'orgStudyIdInfo': {'id': 'MSD-IDREC-C1-2014-152'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'Simulation training', 'description': 'Addition of simulation training during usual clinical training as part of a GMC (General Medical Council) recognised Deanery training programme', 'interventionNames': ['Behavioral: Simulation training']}, {'type': 'NO_INTERVENTION', 'label': 'Non-simulation/Routine training', 'description': 'Usual clinical training as part of a GMC (General Medical Council) recognised Deanery training programme'}], 'interventions': [{'name': 'Simulation training', 'type': 'BEHAVIORAL', 'description': 'Simulation training in a skills lab for 1 hour per week over 13 weeks on dry, bench-top box trainers and anatomical simulators', 'armGroupLabels': ['Simulation training']}]}, 'contactsLocationsModule': {'locations': [{'zip': 'OX3 7LD', 'city': 'Oxford', 'state': 'Oxfordshire', 'country': 'United Kingdom', 'facility': 'Nuffield Orthopaedic Centre', 'geoPoint': {'lat': 51.75222, 'lon': -1.25596}}], 'overallOfficials': [{'name': 'Jonathan L Rees, FRCS-Tr&Orth', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'University of Oxford'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University of Oxford', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}