Hospital stay of inpatients HIV/AIDS with mental and behavioral disorders: 2007 vs 2010 discharges in the United States
Bibliographic record
Abstract
About 1.1 million Americans are living with HIV, and 21% of these persons do not know they are infected. African Americans are 8.6 times more likely to be diagnosed with HIV infection, as compared to the White population in 2008. Women account for a 25% of all new HIV/AIDS cases, with approximately 65% having contracted the infection via heterosexual contact. People with mental and behavioral disorders are at increased risk for HIV/AIDS. Tendencies for committing risky behavior that leads to HIV infection is around 50% among people who are mentally ill. Current nation-based statistics of factors that affect the outcomes of inpatients HIV/AIDS admitted to non-federal hospitals is lacking. A retrospective data analysis of discharges was conducted from the 2007 and 2010 Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS). Sample selection criteria was done using the International Classification of Diseases, Ninth Revision (ICD-9) codes (HIV/AIDS = 042, Schizophrenia = 295, Depression = 296, Bipolar = 657). Statistical Analysis involved the use of crosstab, and multiple regression for patient measures (age, gender, ethnicity, income, insurance, patients’ co-morbidities, admission types, treatment procedures); hospital measures (teaching status, ownership, location, region, size) and length of stay (LOS). The result of this study has shown that the patient demographics age, gender, ethnicity, insurance, location and size or region of the hospitals were not the most critical variables that made the patients stay longer on admission to non-federal hospitals. These findings highlight the conversation that sicker patients with more comorbidities are usually admitted to teaching hospitals where more Registered Nurses (RNs) are employed full time for a more positive and satisfactory patient outcomes. Finally, the result confirmed the relationship between the ratio of RNs FTEs and patient LOS. This has clinical implications in hiring, retention and expectations as it regards to inpatients HIV/AIDS outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".