Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
500 participants
OBSERVATIONAL
2024-09-30
2027-09-30
Brief Summary
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We humans do a poor job when classifying the injury related to a patient sustaining a proximal humeral fracture. In consequence, there is great heterogeneity in the treatment of proximal humerus fractures. Moreover, offering relevant information to patients regarding the risk of complications or fracture sequelae is challenging, given that the current series are based on obsolete classifications, and the published series bring together just over hundreds of patients analyzed. With these limitations, patients have few opportunities to participate in decision-making about their injury.
The present project aim is to integrate new technologies for the prediction of relevant clinical results for the patients presenting a proximal humeral fracture. In brief, AI can help identify similar fracture patterns without human inference, while humans can feed the algorithm with variables of interest such as the functional outcomes and complications related to this particular type of fracture.
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Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Interventions
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Use of IA for proximal humeral fracture prognosis
None (prognosis study)
Eligibility Criteria
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Inclusion Criteria
Subjects evaluated within the first 3 weeks after the injury. Patients between 18 and 90 years of age. Patients who have been studied with simple shoulder radiographs in anteroposterior and scapular outlet projections.
Participants who accept 1-year time follow-up.
Exclusion Criteria
Patients who have previously received surgical treatment on the affected limb. Patients who have suffered a previous fracture in the affected limb. Surgically treated patients.
18 Years
90 Years
ALL
No
Sponsors
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Parc de Salut Mar
OTHER
Parc Taulí Hospital Universitari
OTHER
Consorci Sanitari de l'Anoia
OTHER
Responsible Party
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Joan Miquel
PhD
Other Identifiers
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2024/5001
Identifier Type: -
Identifier Source: org_study_id
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