Antidepressant’s long-term effect on cognitive performance and cardiovascular system التأثير طويل المدى لمضادات الاكتئاب على الأداء الإدراكي والجهاز القلبي الوعائي
Bibliographic record
Abstract
Background: The nature of antidepressants and their adverse effects should be considered when treating severe depression in individuals with psychotic symptoms. Antidepressant prescription rates have risen steadily over the last 30 years, affecting people of all ages.Aim: The goal of this study was to see if depression and antidepressant usage were linked to long-term changes in cognitive function and cardiovascular health.Methodology: Meta-analysis was performed using PRISMA guidelines along with using the SPIDER search framework using related keywords on different search engines ie. Google scholars, PubMed, Scopus, ISI, etc. Total (n=2256) papers were obtained and assessed for eligibility. Altogether 15 studies were included using databases and other methods. The Newcastle-Ottawa Scale examined the grades provided by the data after numerous screenings.Result: A distinct link was found between antidepressants with cognitive performance and the cardiovascular system. Dementia and hypertension were prevailing long-term effects caused by frequent use of antidepressants in chronic and mild depression. ملخصالخلفية: ينبغي مراعاة طبيعة مضادات الاكتئاب وآثارها الجانبية عند علاج الاكتئاب الحاد لدى الأفراد الذين يعانون من أعراض ذهانية. وقد ارتفعت معدلات وصف مضادات الاكتئاب بشكل مطرد على مدار الثلاثين عامًا الماضية، مما أثر على الأشخاص من جميع الأعمار.الهدف: هدفت هذه الدراسة إلى معرفة ما إذا كان الاكتئاب واستخدام مضادات الاكتئاب مرتبطين بتغيرات طويلة المدى في الوظيفة الإدراكية وصحة القلب والأوعية الدموية.المنهجية: أُجري تحليل تلوي باستخدام إرشادات PRISMA، بالإضافة إلى إطار عمل البحث SPIDER، باستخدام كلمات مفتاحية ذات صلة في محركات بحث مختلفة، مثل Google Scholastics وPubMed وScopus وISI، وغيرها. تم الحصول على إجمالي (ن = 2256) ورقة بحثية وتقييمها للتحقق من أهليتها. أُدرجت 15 دراسة باستخدام قواعد البيانات وطرق أخرى. فحص مقياس نيوكاسل-أوتاوا الدرجات المقدمة من البيانات بعد العديد من الفحوصات.النتيجة: وُجدت صلة واضحة بين مضادات الاكتئاب والأداء الإدراكي والجهاز القلبي الوعائي. كان الخرف وارتفاع ضغط الدم من الآثار طويلة المدى السائدة الناتجة عن الاستخدام المتكرر لمضادات الاكتئاب في حالات الاكتئاب المزمن والخفيف.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".