Health and Socioeconomic Status Differences Among Antibody Hepatitis C Positive and Negative
Bibliographic record
Abstract
Objective: To characterize the socioeconomic and health status, disease symptoms of anti-HCV-positive and negative transfusion recipients. Methods: A cross-sectional interviewer-administered survey of subjects identified through the British Columbia Blood Recipient Program. Study subjects were 18 years and over and had to have had a transfusion between August 1, 1986 and June 30, 1990 and completed an interview of satisfactory quality. Anti-HCV-positive subjects were those seeking monetary compensation from the provincial and Canadian governments and the comparison group was randomly selected from a pool of anti-HCV-negative subjects. The study was designed to detect an assumed difference of 20 % in signs and symptoms between the two groups. Statistical comparisons were conducted using bivariate and multivariate logistic regression analyses. Results: A total of 241 and 222 anti-HCV-positive and negative subjects were respectively interviewed and met the study’s eligibility criteria. Results from the multivariate analysis indicated that anti-HCV-positive recipients were more likely to have two or more clinical symptoms (OR = 3.53; 95 % CI: 1.44, 8.70), to be in worse health status as compared to ten
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 | 0.000 |
| 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) | 0.002 | 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".