Last reviewed · How we verify
NCT06120478
Prediction of Risk Factors for Adverse Events After Head and Neck Vascular Recanalization Surgery Based on Machine Learning Models
trial testing observe in Machine Learning in 1,300 participants. Completed in 1 October 2023.
1 November 2022
Quick facts
| Lead sponsor | Tang-Du Hospital |
|---|---|
| Status | Completed |
| Study type | OBSERVATIONAL |
| Enrollment | 1,300 |
| Start date | 1 January 2019 |
| Primary completion | 1 November 2022 |
| Estimated completion | 1 October 2023 |
| Sites | 1 location across China |
Drugs / interventions tested
- observe
Conditions studied
- Machine Learning — all drugs for Machine Learning →
Sponsor
Tang-Du Hospital
Who can join
Adults 18 to 90, any sex, with Machine Learning. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
Prediction of risk factors for adverse events after head and neck vascular recanalization surgery based on machine learning models
Publications & conference data
1 peer-reviewed publication reference this trial (live from Europe PMC):
-
Integrating untargeted metabolomics and deep learning approaches to identify specific metabolic signatures and new mechanisms in unstable plaques.
Ma JQ, Wang L, Qu XP, Zhang Y, et al · · 2026 · PMID 42205783 · DOI 10.3389/fcvm.2026.1646067
Verify or expand the search:
- PubMed search for NCT06120478
- Europe PMC full search
- ASCO Meeting Library
- ESMO Meeting Library
- bioRxiv preprints
- medRxiv preprints
- Google Scholar
Related trials
Other trials of observe
Trials testing the same drug.
- NCT05629403 — Exclusive Breastfeeding Improves Puerperal Glucose Metabolism in Pregnant Women With Gestational Diabetes Mellitus and L · completed
- NCT07512609 — Environment, Inflammation and Metabolic Diseases Study · recruiting
Other recruiting trials for Machine Learning
Currently open trials in the same condition.
- NCT07509632 — Predicting Pathological Complete Response in Rectal Cancer Using Machine Learning · active not recruiting
- NCT07521111 — Predictive Value of Gastrointestinal Blood Flow for Enteral Nutrition Intolerance in Critically Ill Patients · recruiting
- NCT07531446 — Construction of a Clinico-Imaging Collaborative Diagnostic Model for Dermatomyositis Combined With Interstitial Lung Dis · active not recruiting
- NCT07129616 — Remote Monitoring of Asthma in Children and Young People · recruiting
- NCT07256548 — Machine Learning for Predicting Spinal Anesthesia Duration · recruiting
Other Tang-Du Hospital trials
Trials by the same sponsor.
- NCT07528235 — An Open-Label, Dose-Escalation Study on the Safety, Tolerability and Preliminary Efficacy for Preventing SAP by Intraven · Phase 1 · not yet recruiting
- NCT07516522 — Intra-arterial Methylprednisolone After Endovascular Thrombectomy · Phase 2 · recruiting
- NCT07354061 — Neoadjuvant Therapy With Ensartinib Combined With Chemotherapy for ALK-positive Non - Small Cell Lung Cancer (NSCLC) · Phase 1, PHASE2 · recruiting
- NCT07327736 — Anesthesia Comparison in Early-stage Small NSCLC: A Multicenter RCT · NA · not yet recruiting
- NCT07452601 — Time-of-Day of Immunotherapy Infusion in Neoadjuvant Immunochemotherapy for Thoracic ESCC · Phase 2 · not yet recruiting
Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT06120478 (US National Library of Medicine, public domain)
- Publications: Europe PMC API search by NCT ID, retrieved 9 June 2026
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by Tang-Du Hospital
- Last refreshed: 16 April 2025
Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT06120478.
Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing