Effect of metastatic site on emergency department disposition in men with metastatic prostate cancer.
Bibliographic record
Abstract
INTRODUCTION: Though the prevalence of metastatic prostate cancer is decreasing, the rate of admission from the emergency department (ED) is increasing. Little is known about the implications of metastatic site on a patient's ED course and admission. MATERIALS AND METHODS: A weighted estimate of 15,367 patients with metastatic prostate cancer who presented to the ED between January 1, 2006 and December 31, 2009 was abstracted from the Nationwide Emergency Department Sample (NEDS). Descriptive statistics were used to elaborate patient and hospital characteristics of the metastatic prostate cancer population and logistic regression models were fitted to identify predictors of admission. RESULTS: The most common site of metastasis in patients with metastatic prostate cancer presenting to the ED was bone (80.6%), followed by liver (13.2%), lung (9.3) and other genitourinary sites (8.1%). Over the study period, there was an increase in prevalence of the four commonest metastatic sites, and admission rates varied between metastatic sites (83.2% for bone to 95.2% for nodal metastasis). Substantial variability in the rate of inpatient mortality was noted. Increasing age, Northeast region, increased comorbidity burden, and the presence of nodal metastases and other urinary metastases were shown to be independent predictors of hospital admission. CONCLUSIONS: The commonest metastatic site in patients presenting to United States EDs with metastatic prostate cancer between 2006 and 2009 was bone. Patients presenting with nodal metastases were most likely to be admitted. Independent predictors of hospitalization included age, Northeast region, increased comorbidities, nodal metastases and other urinary metastases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".