S3261 Incidence of Acute Hepatitis A, B, C, and Co-Infection in Southeast Appalachian, KY
Bibliographic record
Abstract
INTRODUCTION: The aim of this project is to evaluate coinfection of hepatitis A, B, C in Southeast Appalachian, Kentucky. METHODS: Clinical data on 152 hepatitis cases between January and April of 2019 were collected retrospectively and analyzed by age, sex, risk factors, and coinfections. RESULTS: For hepatitis A, 56.25% were male, hepatitis B 58.3% male, and C 70.24% male. The median ages for males with hepatitis A was 43, females 56; hepatitis B 50, female 52; and C 43, female 40.5. Most (79.6%) patients were diagnosed with hepatitis C and of those, 70% were male. Five (13.2%) patients had more than one type of hepatitis; A and B 3.3%, A and C (3.3%), B and C (3.3%), and A, B, C (3.3%). Patients with hepatitis A, B and C were all male. Risk behaviors among the patients included reported IV drug use (35%), and alcohol abuse (12.5%). There was a low incidence of confections (13.2%). Tattoo users had a higher incidence of hepatitis B and hepatitis C than hepatitis A. Approximately 33% of drug users were associated with hepatitis A, B, and C. CONCLUSION: In our findings, the incidence of hepatitis C (79.6%) was higher than A (10.5%) or B (7.9%). Incidence was higher in males than female; strikingly for hepatitis C. Further study with a higher number of patients might give more insight. Counseling and vaccination may prevent future incidents of hepatitis.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".