Features and long‐term outcomes of patients hospitalized for diabetic ketoacidosis
Bibliographic record
Abstract
BACKGROUND: Diabetic ketoacidosis (DKA) is an acute metabolic complication characterized by hyperglycaemia, ketones in blood or urine, and acidosis. OBJECTIVE: The aim of this study was to characterize features of patients hospitalized for DKA, to identify triggers for DKA and to evaluate the long-term effects of DKA on glycaemic control, complications of diabetes, re-hospitalizations, and mortality. METHODS: Historical prospectively collected data of patients hospitalized to medical wards for DKA between 2011 and 2017. Data regarding comorbidities, mortality, triggers, and re-hospitalizations for DKA were also collected. RESULTS: The cohort consisted of 160 patients (mean age 38 ± 18 years, 43% male). One fifth of the patients (34 patients, 21%) were newly diagnosed with diabetes, and DKA was their first presentation of the disease. Among the 126 patients with pre-existing diabetes, the common identified triggers for DKA were poor compliance to treatment (22%) and infectious diseases (18%). During over 7 years of follow-up, mortality rate was 9% (15 patients), and re-hospitalization for DKA rate was 31% (50 patients). Risk factors for re-hospitalization for DKA included young age (OR = 1.02, 95% CI, 1.00-1.04), pre-existing diabetes compared to DKA as the first presentation (OR = 5.4, 95% CI, 1.7-18), and poorer glycaemic control before initial hospitalization (10.5 ± 2.5% vs. 9.4 ± 2.2%; OR = 0.8, 95% CI [0.68-0.96]) and after discharge (10.3 ± 2.4% vs. 9.0 ± 1.9%; OR = 0.73, 95% CI [0.61-0.87]). Laboratory tests during the initial hospitalization, smoking, alcohol, or comorbidities did not increase the risk for re-hospitalization for DKA. CONCLUSIONS: The risk for readmission for DKA is higher for young patients with long duration of diabetes, poor compliance of insulin treatment and poorly controlled diabetes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".