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All Journal Jurnal Klinik dan Riset Kesehatan Indonesian Journal of Cancer
Pradyaputri, Naura Shafa
Unknown Affiliation
Biochemistry, Genetics & Molecular Biology Health Professions Immunology & microbiology Medicine & Pharmacology Nursing Public Health

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Hubungan Tingkat Kecemasan dengan Angka Kejadian Nokturia pada Mahasiswa/i Fakultas Teknologi Pertanian Universitas Brawijaya selama Pandemi COVID-19 Pradyaputri, Naura Shafa; Daryanto, Besut; Sagita, Zendy
Jurnal Klinik dan Riset Kesehatan Vol 3 No 2 (2024): Edisi Februari
Publisher : RSUD Dr. Saiful Anwar Province of East Java

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/jk-risk.03.2.3

Abstract

Pendahuluan: Nokturia merupakan gejala paling umum dan mengganggu pada gangguan saluran kemih bagian bawah. Dampak nokturia dirasakan lebih besar pada usia muda khususnya mahasiswa yang dapat menyebabkan penurunan performa akademik. Sebuah penelitian mengungkapkan bahwa kecemasan sering terjadi secara bersamaan dengan nokturia dan memiliki hubungan dua arah. Tujuan: Penelitian ini bertujuan untuk mengetahui prevalensi serta hubungan tingkat kecemasan dengan angka kejadian nokturia pada mahasiswa/i teknologi pertanian selama pandemi COVID-19. Metode: Penelitian observasional analitik dengan desain cross sectional. Kuisioner Google form berisi pertanyaan data karakteristik mahasiswa, kuesioner ICIQ-N berisi frekuensi nokturia serta skor mengganggu, dan kuesioner ZSAS untuk tingkat kecemasan. Analisis data menggunakan uji Chi Square untuk menganalisis hubungan antara tingkat kecemasan dan nokturia. Hasil: Hasil diperoleh bahwa terdapat hubungan yang tidak signifikan antara tingkat kecemasan (p=0.307). Nokturia merupakan gejala yang sering dialami oleh mahasiswa. Skor mengganggu meningkat seiring dengan tingkat keparahan nokturia. Kesimpulan: Terdapat hubungan yang lemah antara tingkat kecemasan dengan nokturia.
Hubungan Tingkat Kecemasan dengan Angka Kejadian Nokturia pada Mahasiswa/i Fakultas Teknologi Pertanian Universitas Brawijaya selama Pandemi COVID-19 Pradyaputri, Naura Shafa; Sagita, Zendy; Daryanto, Besut
Jurnal Klinik dan Riset Kesehatan Vol 3 No 2 (2024): Edisi Februari
Publisher : RSUD Dr. Saiful Anwar Province of East Java

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/jk-risk.03.2.3

Abstract

Background: Nocturia is the most common and disturbing symptom in lower urinary tract disorders. Younger people, particularly college students, are more affected by nocturia, which can have a negative impact on their academic performance. A study revealed that anxiety often occurs simultaneously with nocturia and has a reciprocal association. Aim: The purpose of this study is to examine the frequency of nocturia in agricultural technology students during the COVID-19 pandemic and the association between anxiety levels and nocturia incidence. Methods: Analytic observational study with cross sectional design. The Google form questionnaire contains questions about student characteristics data, the ICIQ-N questionnaire contains the frequency of nocturia and disturbance scores, and the ZSAS questionnaire for anxiety levels. Data analyzed used Chi Square test to analyze the relationship between anxiety level and nocturia. Results: The results showed that there was an insignificant relationship between anxiety levels (p=0.307). Nocturia is a symptom that is often experienced by students. The disturbing score increases along with the severity of nocturia. Conclusion: There was a weak association between anxiety level and nocturia.
Potential Efficacy of Artificial Intelligence in Mammography for Breast Cancer Screening: Current Evidence from Meta-Analysis Amalia, Nurlinah; Nurdiana, Farah; Pradyaputri, Naura Shafa
Indonesian Journal of Cancer Vol 19, No 4 (2025): December
Publisher : http://dharmais.co.id/

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33371/ijoc.v19i4.1353

Abstract

Background: Artificial intelligence (AI), an advancing field of data science, has been applied in mammography screening for early detection of breast cancer in an effort to enhance screening participants' outcomes. Screening is crucial to halting the spread of breast cancer. These days, mammography is typically used in screenings conducted by radiologists. Therefore, alternative diagnostic methods are needed to provide a diagnostic solution that is efficient in terms of both time and resources. This review aims to evaluate the accuracy of AI applications in radiology, specifically in mammographic image interpretation, to determine whether AI can serve as an evidence-based recommendation for breast cancer screening. Methods: We conducted a systematic review and meta-analysis following the PRISMA guidelines. Literature searches were performed across multiple databases, including PubMed, ScienceDirect, and SpringerLink. The inclusion criteria were based on the PICOs framework, focusing on individuals at risk of breast cancer undergoing mammographic screening, where AI was used to interpret the images and compared to a radiologist. Exclusion criteria included studies involving patients with diagnosed breast cancer, non-human studies, non-English, books, paid articles, and review articles. The primary outcomes of interest were the sensitivity and specificity of AI in detecting breast cancer from mammograms. Meta-analysis was conducted using STATA software, while the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool was employed to evaluate study qualityResults: A total of 2,412,102 mammograms from twenty-six studies were included in this analysis. The results indicated that AI demonstrated moderate sensitivity [84% (99.92% CI: 99.91 – 99.92)] and specificity [87% (99.97% CI: 99.97 – 99.97)] with a p-value (0.001). Conclusions: These results suggest that AI has potential as a breast cancer diagnosis tool in the future. Radiologists can become more accurate with AI algorithms, which are useful for screening, cutting down on unnecessary recall rates, and reducing effort.
Co-Authors Amalia, Nurlinah Nurdiana, Farah Sagita, Zendy