Publications by authors named "Marcela Ewing"

Background: Primary health care (PHC) is often the first point of contact when diagnosing colorectal cancer (CRC). Human limitations in processing large amounts of information warrant the use of machine learning as a diagnostic prediction tool for CRC.

Aim: To develop a predictive model for identifying non-metastatic CRC (NMCRC) among PHC patients using diagnostic data analysed with machine learning.

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Background: Early detection of colorectal cancer (CRC) is crucial for survival. Primary care, the first point of contact in most cases, needs supportive risk assessment tools. We aimed to replicate the Swedish Colorectal Cancer Risk Assessment Tool (SCCRAT) for non-metastatic CRC in primary care and examine if risk factor patterns depend on sex and age.

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Background: Lung cancer (LC) kills more people than any other cancer globally, mainly due to the late stage of diagnosis.

Aim: To identify and quantify the prediagnostic features of non-metastatic lung cancer (nMLC) and to compare the clinical features in GPs' chest X-ray referral letters with the clinical features (expressed as diagnostic codes) in medical records.

Design & Setting: A population-based case-control study was conducted using diagnostic codes from national and regional healthcare databases in Sweden.

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Background: Many patients with common cancers are late diagnosed.

Objectives: Identify consultation profiles and clinical features in patients with the seven most common cancers, who had consulted a general practitioner (GP) frequently before their cancer diagnosis.

Methods: A case-control study was conducted in Region Västra Götaland, Sweden.

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Background: Colorectal cancer is the third most common cancer worldwide and second most common in Europe. Despite screening, it is often diagnosed at an unfavourable stage.

Aim: To identify and quantify features of non-metastatic colorectal cancer in primary care to enable earlier diagnosis by GPs.

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Objective: To identify early diagnostic profiles such as diagnostic codes and consultation patterns of cancer patients in primary care one year prior to cancer diagnosis.

Design: Total population-based case-control study.

Setting And Subjects: 4562 cancer patients and 17,979 controls matched by age, sex, and primary care unit.

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