Predictors of Recurrent Hospital Admission for Patients Presenting With Diabetic Ketoacidosis and Hyperglycemic Hyperosmolar State
Bibliographic record
Abstract
BACKGROUND: Diabetic ketoacidosis (DKA) and hyperglycemic hyperosmolar state (HHS) are two serious, preventable complications of diabetes mellitus. Analysis of variables associated with recurrent DKA and HHS admission has the potential to improve patient outcomes by identifying possible areas for intervention. The aim of this study was to evaluate potential predictors of recurrent DKA or HHS admission. METHODS: This was a retrospective case-control study of 367 patients presenting during a 5-year period with DKA or HHS at a US tertiary academic medical center. Six potential readmission risk factors identified via literature review were coded as "1" if present and "0" if absent. Readmission odds ratios (ORs) for each risk factor and for the combined score of significant risk factors were calculated by logistic regression. RESULTS: Readmission odds were significantly increased for patients with age < 35, history of depression or substance/alcohol abuse, and self-pay/publicly funded insurance. HbA1C > 10.6% on admission and ethnic minority status did not significantly increase readmission odds, with inadequate study power for these variables. A total "ABCD" score, based on Age (< 35 years), Behavioral health (depression), insurance Coverage (self-pay/publicly funded insurance), and Drug/alcohol abuse, also had a significant effect on readmission odds. CONCLUSIONS: Consideration of individual risk factors and the use of a scoring system based on objective predictors of recurrent DKA and HHS admission could be of value in helping identify patients with high readmission risk, allowing interventions to be targeted most effectively to reduce readmission rates, associated morbidity, and mortality.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".