ArticleBMC cancer2026
Clinicopathologic characteristics and predictors of treatment uptake among patients with cervical cancer managed at Jimma University Medical Center, Southwest Ethiopia.
Article in BMC cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
backgroundCervical cancer remains a major public health concern in Ethiopia because of limited screening and delayed diagnosis, resulting in high mortality. Most patients present with advanced-stage disease, predominantly squamous cell carcinoma. Despite the World Health Organization’s 90%–70%–90% strategy, screening coverage is low, and access to treatment remains inadequate. This study assessed the clinicopathologic characteristics and predictors of treatment uptake among cervical cancer patients managed at Jimma University Medical Center in southwest Ethiopia to inform evidence-based interventions.
methodsA cross-sectional study was conducted at Jimma University Medical Center, including all women with histologically confirmed cervical cancer between April 2019 and October 2024. Data on demographics, clinical presentation, pathology, and outcomes were collected using a pretested tool and entered into REDCap. Missing outcome data were supplemented through telephone interviews. Multiple imputation was used for missing explanatory variables (n = 20 datasets). Statistical analysis was performed using STATA version 17, univariable and multivariable logistic regression to identify predictors of treatment uptake.
resultAmong the 719 women with cervical cancer, most were illiterate, grand multiparous, and from rural areas. Postmenopausal bleeding and pelvic pain were the most common presenting symptoms. Over two-thirds experienced delays in seeking care exceeding 90 days, and only 16% had a history of prior cervical cancer screening. Of the 709 patients with documented stage, 78.4% presented with advanced disease (stage IIB–IV)), while 21.6% were diagnosed at an early stage (stage I–IIA). Chemoradiotherapy was the main treatment modality, while surgery was rarely performed. Treatment uptake was higher among women younger than 55 years (OR = 2.27; 95% CI: 1.08–4.75) and those with formal education (OR = 3.31; 95% CI: 1.25–8.70), whereas women diagnosed at stage IV were significantly less likely to receive treatment (OR = 0.10; 95% CI: 0.03–0.43).
conclusionThese findings underscore the need to strengthen culturally appropriate community awareness and expand accessible screening programmes, particularly in rural areas, to improve timely treatment and outcomes for women in middle age. Vaccination and palliative care services should also be integrated into comprehensive cervical cancer care.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.