Tundra Space

Tundra Space

Clinical Research Directory

Browse clinical research sites, groups, and studies.

6 clinical studies listed.

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Patient

Tundra lists 6 Patient clinical trials. Each listing includes eligibility criteria, study locations, and direct links to research sites in the Tundra directory.

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RECRUITING

NCT07465848

Prospective Multicentre Study for the Application of Telomere Measurement Technology as a Tool for Lung Cancer Diagnosis

This research is a clinical performance study of in vitro diagnostic device (IVD), a prospective, multicentre, open-label, case-control clinical investigation aimed at constructing a predictive model that allows results to be classified as positive or negative in relation to the probability of having lung cancer.

Gender: All

Ages: 45 Years - Any

Updated: 2026-03-12

7 states

Lung Cancer (Diagnosis)
Health Adult Subjects
Patient
ACTIVE NOT RECRUITING

NCT04129138

Patient and Caregiver Attitudes and Beliefs Regarding Prescription of Intranasal Naloxone Spray for Opioid Overdose

This trial studies the attitudes and beliefs of high risk patients and caregivers regarding the prescription of intranasal naloxone spray for opioid overdose. Knowledge regarding high risk patients' and caregivers' beliefs and attitudes regarding co-prescription of naloxone spray with opioids may help to identify barriers to prescribing and helping tailor the education to better meet the needs of patients and caregivers.

Gender: All

Ages: 18 Years - Any

Updated: 2025-10-14

1 state

Caregiver
Patient
ENROLLING BY INVITATION

NCT06670859

The Effect of Mobilization Coverall on Anxiety, Comfort, Physical Privacy and Satisfaction

This study is designed as crossover randomized controlled trial to examine the effect of a mobilization coverall designed for intensive care patients on anxiety, comfort, physical privacy and satisfaction.

Gender: All

Ages: 18 Years - Any

Updated: 2025-09-17

Intensive Care Unit
Mobility
Patient
RECRUITING

NCT06525181

AI as an Aid for Weekly Symptom Intake in Radiotherapy

The study investigates the use of artificial intelligence (AI) and large language models (LLMs) to enhance the efficiency and accuracy of weekly treatment consultations (OTVs) in radiotherapy. It hypothesizes that an AI-enabled symptom summary tool will match traditional medical review methods in accuracy while saving time. The study includes patients undergoing pelvic radiotherapy and excludes those with pelvic reirradiation or who have undergone surgery. Patients will receive both standard and AI-assisted weekly consultations, with AI summaries generated using the OpenAI GPT-4 API. Blinded oncologists will compare the accuracy and quality of the AI-generated and doctor-generated summaries, while patients and doctors will rate these summaries. The primary objective is to evaluate the accuracy and time efficiency of AI-assisted symptom summaries compared to traditional methods.

Gender: All

Ages: 18 Years - Any

Updated: 2024-10-10

Radiotherapy Side Effect
Pelvic Cancer
Patient
RECRUITING

NCT06369103

The Sedative Effects of Dexmedetomidine VS Propofol During Peribulbar Anesthesia in Patients Undergoing Vitrectomy Surgery

The aim of the work to compare the Sedative Effects of Dexmedetomidine and Propofol during peribulbar anesthesia in patients undergoing vitrectomy surgery on hemodynamic, respiratory parameters, recovery profile, analgesic effects, post-operative cognitive function, patients satisfaction and adverse events.

Gender: All

Ages: 20 Years - 70 Years

Updated: 2024-04-16

1 state

Patient
Satisfaction
RECRUITING

NCT05288140

Efficacy of the Use of Diaries in Intensive Care Units

Randomized clinical trial that aims to evaluate the impact of the use of a diary in patients and relatives of patients admitted to an ICU in relation to usual practice in terms of health-related quality of life, the post-traumatic stress and anxiety/depression at 2, 6 and 12 months after ICU discharge.

Gender: All

Ages: 18 Years - 100 Years

Updated: 2024-03-27

Diary (Blank Journal)
Intensive Care Unit
Patient
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