ArticleApplied clinical informatics2024
Comparing Clinician Estimates versus a Statistical Tool for Predicting Risk of Death within 45 Days of Admission for Cancer Patients.
Article in Applied clinical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed.
- Review
- Responsible Artificial Intelligence governance in oncology.NPJ digital medicine · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
objectivesWhile clinical practice guidelines recommend that oncologists discuss goals of care with patients who have advanced cancer, it is estimated that less than 20% of individuals admitted to the hospital with high-risk cancers have end-of-life discussions with their providers. While there has been interest in developing models for mortality prediction to trigger such discussions, few studies have compared how such models compare with clinical judgment to determine a patient's mortality risk.
methodsThis study is a prospective analysis of 1,069 solid tumor medical oncology hospital admissions (
resultsWithin 45 days following hospital admission, 229 (25%) of 911 patients died. The model performed better than the clinician estimates (AUC 0.834 vs. 0.753,
conclusionThe inpatient prognosis at admission model is a robust tool to assist clinical providers in evaluating mortality risk, and it has recently been implemented in the electronic medical record at our institution to improve end-of-life care planning for hospitalized cancer patients.
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