Acute lymphocytic leukemia severity and mortality hospitalizations in the United States: A population-based study.
Bibliographic record
Abstract
Objectives: The objective of the study was to understand the relationship between social and economic indicators of health and its association with hospitalization severity and mortality risk among ALL patients. Methods: In this retrospective study, hospitalizations with primary and secondary diagnosis were identified using International Classification of Diseases (ICD 10) codes (C91.00, C91.01, C91.02) of ALL in the National Inpatient Sample (NIS) between 2016 and 2018. Hospitalization outcomes such as LOS, mortality, severity and mortality risk, cost, diagnosis (NDX), number of procedures (NPR) were analyzed by race and ethnicity, household income, and patient location among patients with primary and secondary ALL diagnoses. Results: A total of 158090 hospitalizations were identified as meeting the inclusion criteria without missing cases with a primary or secondary diagnosis of ALL using ICD-10 codes. Severity risk at presentation varied from one area to the next, with the highest rate (per 10K) presentations in the New England region for both extreme likelihood (778) and extreme loss of function (2198) at presentation. Mortality and severity among uninsured patients were the second highest (614, 2193) compared to other payers. Extreme mortality risk at presentation was higher among African American (711), Caucasian (648), and Native American (612) populations compared to other racial and ethnic groups. Conclusion: The findings of this study suggest a relative decrease in presentation rate by year and higher mortality among specific groups-based demographics indicators. It also confirms the impact of advanced therapeutics and improved severity and mortality among younger populations with ALL compared to the older population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".