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 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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".