Hospitalized patients co-diagnosed with infective endocarditis and opioid drug dependence in Florida, 2015-2018
Bibliographic record
Abstract
Objective: As the opioid addiction epidemic continues to grow, other serious health issues regarding drug use has also increased. This study examines the trends in admissions and population characteristics of those who experience infective endocarditis with opioid drug dependence.Methods: We used ICD-9-CM and ICD-10-CM codes to identify patients admitted to a hospital with infective endocarditis and with a secondary diagnosis of opioid use related disorders using data released by the Florida Agency for Health Care Administration (AHCA). Data included age, gender, ethnicity, race, discharge disposition, admission type, payer status, total charges, and zip code of patients’ residence.Results: During the four-year period, the percent of patients diagnosed with infective endocarditis and a diagnosis code associated with opioid abuse or dependence doubled (4.48% to 8.52%). Of the patients dually diagnosed, the mean age was 37.47 and the majority were white (90.78%), non-Hispanic (91.96%), and female (58.55%). Nearly 47% of the patients did not have health insurance. The percentage of patients with both diagnosis codes living in urban counties was 91.37%. Median length of stay was 10 days and median total charges for patients was $101,604.Conclusions: With the increasing incidence of opioid dependence and addiction within the United States, there is a rise in infective endocarditis, a costly and debilitating disease. Our analysis provides the framework for hospital systems to identify patients who may benefit from addiction services, which through downstream effects will cause less of a health and financial burden.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".