Human Immunodeficiency Virus (HIV) – An Analysis of Trends in HIV Diagnoses from 2008 – 2018
Bibliographic record
Abstract
Objective: An estimated 1.1 million people are living with the human immunodeficiency virus (HIV) in the United States. Despite over two decades of research, a cure for HIV has not been approved and it remains a pandemic. This research study was conducted to determine the statistical significance in HIV incidence based on diagnoses in 2008 versus 2018; age groups 25-34 years old versus 55+ years old; Black versus Hispanic versus White; male versus female; and geographical location. Methods: This retrospective study was conducted using data from the Center for Disease Control and Prevention (CDC) Atlas Plus data sets, a collection of surveillance data from previous years. Analysis was done using paired t-test for prevalence comparison by year and unpaired t-test for age and sex. ANOVA test was used to compare prevalence by race. Descriptive analysis was done using z-scores to determine differences in HIV rates by state. Results: Incidence by rate from 2008 versus 2018 using a 2-tailed t-test resulted as t50=1.99, P=.052 indicating no statistical significance in incidence in comparison. Analysis of incidence in age groups 25-34 versus 55+ resulted as t50=9.69, P<.001, indicating a statistical significance. Analysis of incidence by race resulted as F2,150=46.23, P<.001, indicating a statistically significant difference between races. Analysis of incidence by sex resulted as t50=7.80, P<.001, indicating a statistically significance difference between males and females. Analysis of incidence in states using descriptive analysis resulted as mean 10.67 (SD 7.21). Outliers include District of Columbia with z-score 3.32 and southern states Florida, Georgia, and Louisiana with z-score 2.07, 2.57,and 2.06 respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".