Comparison of Late HIV Diagnosis as a Marker of Care for Aboriginal Versus Non‐Aboriginal People Living with HIV in Ontario
Bibliographic record
Abstract
BACKGROUND: Studies have found that Aboriginal people living with HIV/AIDS (APHAs) are more likely than non-APHAs to receive suboptimal HIV care, yet achieve similar clinical outcomes with proper care. OBJECTIVE: To compare the proportions of individuals diagnosed late with HIV between APHAs and non-APHAs within the Ontario HIV Treatment Network Cohort Study (OCS). METHODS: The analysis included OCS participants who completed the baseline visit by November 2009. Two definitions of the outcome of late HIV diagnosis were used: the proportion of participants with an AIDS-defining illness (ADI) before or within three months of HIV diagnosis; and the proportion of participants with a CD4(+) count <200 cells/mL at diagnosis. Logistic regression analysis was used to assess the association between Aboriginal ethnicity and late HIV diagnosis. RESULTS: APHAs were more likely to be female and have lower income, education and employment. No statistically significant differences were noted in the proportions receiving a late HIV diagnosis defined by ADI (Aboriginal 5.2% versus non-Aboriginal 6.3%; P=0.40). Multivariate logistic regression analysis revealed a significant association between Aboriginal ethnicity and late HIV diagnosis defined by CD4(+) count after adjusting for age and HIV risk factor (OR 1.55; P=0.04). DISCUSSION: APHAs were more likely to have a CD4(+) count <200 cells/mL at diagnosis but had similar clinical outcomes from late diagnosis when defined by ADI. However, differences may be underestimated due to recruitment limitations and selection bias. CONCLUSION: Additional work is needed to address the socioeconomic and health care needs of APHAs.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".