Variation In Use Of Intensive Care For Adults With Diabetic Ketoacidosis
Bibliographic record
Abstract
Objective-Intensive care unit (ICU) beds are limited, yet few guidelines exist for triage of patients to the ICU, especially patients at low-risk for mortality.The frequency with which lowrisk patients are admitted to ICUs in different hospitals is unknown.Our objective was to assess variation in use of intensive care for patients with diabetic ketoacidosis (DKA), a common condition with a low-risk of mortality.Design-Observational study using the New York State Inpatient Database (2005Database ( -2007)).Setting-159 New York State acute care hospitals.Patients-15,994 adult (≥18) hospital admissions with a primary diagnosis of DKA (ICD-9-CM 250.1x). Interventions-None.Measurements and Main Results-We calculated reliability-and risk-adjusted ICU utilization, hospital length of stay (LOS), and mortality.We identified hospital-level factors associated with increased likelihood of ICU admission after controlling for patient characteristics using multilevel mixed-effects logistic regression analyses; we assessed the amount of residual variation in ICU utilization using the intra-class correlation coefficient.Use of intensive care for DKA patients varied widely across hospitals (adjusted range: 2.1% to 87.7%), but was not associated with hospital LOS or mortality.After multilevel adjustment, hospitals with a high
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".