Development and validation of a clinical prediction model for hospitalization after emergency department admission in patients with cancer
Bibliographic record
Abstract
Background and purpose Emergency department (ED) admissions by cancer patients often result in hospitalization and prolonged ED stays, contributing to overcrowding. Early identification of patients at risk of hospitalization could improve ED flow and ensure timely provision of oncological care. This study aimed to develop and validate models to predict hospitalization among cancer patients admitted to the ED. Methods Adult cancer patients who were admitted to the ED between 1 July 2018 and 30 September 2020 at the Erasmus University Medical Center were included. Data from electronic health records (EHR) were used to develop two logistic regression models: (i) a baseline model including predictors available after ED triage (e.g. patient characteristics, vital parameters) and (ii) an extended model including blood test results. Predictors were selected using the Wald χ 2 statistic. To prevent overfitting, a uniform shrinkage factor was applied. Model performance was assessed with temporal validation (1 October 2019 to 1 January 2020) and evaluated with calibration plots and C-statistics. Results Of 7284 ED admissions, 3967 (54%) resulted in hospitalization. The most common cancers requiring hospitalization were lung, hepatopancreatobiliary, and colorectal cancer. The baseline model included age, sex, primary malignancy, symptoms, metastasis, temperature, pain score, diastolic blood pressure, and heart rate. The model showed good calibration (intercept −0.04, slope 0.86) and discriminative ability [C-statistic 0.71, 95% confidence interval (CI) 0.68-0.74]. The extended model showed improved performance (intercept −0.09, slope 0.92; C-statistic 0.75, 95% CI 0.72-0.78). Conclusion Hospitalization risk of cancer patients admitted to the ED can be predicted using routinely collected EHR data, which could aid in optimizing ED patient flow and ensuring timely provision of oncological services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".