Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Mfululizo wa Kesi za Vituo Nyingi× | Mfululizo wa Kesi× | |
|---|---|---|
| Nyanja | Epidemiolojia | Epidemiolojia |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | Mid-to-late 20th century (collaborative multi-site reporting common by 1970s–1980s) | Longstanding; systematized in 20th century clinical research |
| Mwanzilishi≠ | Evolved from single-center case series practice; formalized in 20th century clinical reporting | Historical clinical practice; formalized in modern evidence-based medicine literature |
| Aina | Observational descriptive study | Observational descriptive study |
| Chanzo asilia≠ | Dekkers, O. M., Vandenbroucke, J. P., Cevallos, M., Renehan, A. G., Altman, D. G., & Egger, M. (2012). COSMOS-E: Guidance on conducting systematic reviews and meta-analyses of observational studies of etiology and prognosis. PLoS Medicine, 9(2), e1001175. link ↗ | Case series. Wikipedia. link ↗ |
| Majina mbadala | multi-site case series, multicentre case series, collaborative case series, multi-institutional case series | case series report, clinical case series, consecutive case series, patient series |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | A multicenter case series is an observational descriptive study in which consecutive or selected patients sharing a defined clinical condition are enrolled and followed at two or more independent clinical sites. By pooling cases across institutions, researchers achieve larger sample sizes and greater demographic and clinical diversity than a single-center series permits, enabling more reliable description of disease presentation, management patterns, and outcomes for rare or uncommon conditions. | A case series is a descriptive observational study that documents the characteristics, clinical course, and outcomes of a group of patients who share a common condition, exposure, or intervention. Unlike case reports, which focus on a single patient, a case series aggregates data across multiple patients (typically three or more) to identify patterns, generate hypotheses, and characterize rare or novel conditions — without a concurrent control group. |
| ScholarGateSeti ya data ↗ |
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