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By integrating the methods used in the assessment of pain in geriatric surgery patients with literature, theory and research, this study aims to: evaluate the effectiveness of the facial diagnosis system in the evaluation of pain after geriatric surgery.
The research hypotheses are as follows:
H1: In the evaluation of pain after geriatric surgery, there is a concordance between the pain score evaluated by the patient and the pain score obtained from facial expression diagnostic system analysis.
H1: In the evaluation of pain after geriatric surgery, there is a correlation between the pain score evaluated by the nurse and the pain score obtained from the analysis of the facial expression diagnosis system.
H1: In the evaluation of pain after geriatric surgery, there is a correlation between the pain score evaluated by the patient and the pain score evaluated by the nurse.
By integrating the methods used in the assessment of pain in geriatric surgery patients with literature, theory and research, this study aims to: evaluate the effectiveness of the facial diagnosis system in the evaluation of pain after geriatric surgery.
The number of patients in the groups will be analyzed based on one of the studies to be used in the study. According to the calculations made in the G-Power 3.1 Demo package program, when the effect size is accepted as 0.8, it was seen that at least 68 patients would be sufficient for 80% power. In this way, the criteria sampling method will be used. Anticipated duration is one year.
The inclusion criteria of the patients in the study are as follows:
Data collecting:
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Patients Group | Geriatric patients in the postoperative period hospitalized in surgical wards |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| No intervention | Other | No Control |
|
| Measure | Description | Time Frame |
|---|---|---|
| Determination of the average scores from the "Wong Baker Facial Pain Scale" | Wong Baker Facial Pain Scale: It consists of 6 different facial expressions, starting with a smiling face and ending with a crying face. The patient and the nurse will be asked to give a pain score to the patient when immediately after the patient comes from the surgery and 1 hour after coming to the service. | 6 months |
| Determination of the average scores from the "Numerical Rating Scale" | Numerical Rating Scale: Patients are asked to draw a number from 0 to 10, 0 to 20, or 0 to 100 that best fits pain intensity. Zero usually indicates "no pain", while the upper limit represents "unbearable pain". The patient and the nurse will be asked to give a pain score to the patient when immediately after the patient comes from the surgery and 1 hour after coming to the service. | 6 months |
| Measure | Description | Time Frame |
|---|---|---|
| Machine learning will be provided using a computer-assisted facial expression recognition system. | The 30-second facial expression of the patient is recorded with a video camera immediately after the patient leaves the surgery and comes to the service and 1 hour after coming to the service. With this video recording and the score given by the patient, the pain score will be uploaded to the computer. The obtained value will be compared with the value given by the nurses. |
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Inclusion Criteria:
Exclusion Criteria:
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The research sample will consist of geriatric patients aged 65 and over who were hospitalized in the post-operative general surgery service.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Tülin KURT ALKAN, Expert | Contact | +905393711902 | tulinkurt0@gmail.com | |
| Nurten TASDEMİR, Doc. Dr. | Contact | +905072344018 | ntasdemir@gmail.com |
| Name | Affiliation | Role |
|---|---|---|
| Tülin KURT ALKAN, Expert | https://zonguldakataturkdh.saglik.gov.tr/?_Dil=1 | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Zonguldak Ataturk State Hospital | Recruiting | Zonguldak | Centre | 67100 | Turkey (Türkiye) |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 39761361 | Derived | Kurt Alkan T, Tasdemir N. Testing Machine Learning-Based Pain Assessment for Postoperative Geriatric Patients. Comput Inform Nurs. 2025 Nov 1;43(11):e01248. doi: 10.1097/CIN.0000000000001248. |
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| ID | Term |
|---|---|
| D010146 | Pain |
| ID | Term |
|---|---|
| D009461 | Neurologic Manifestations |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |
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| 12 months |