The effect of history of injection drug use and alcoholism on HIV disease progression
Bibliographic record
Abstract
The effectiveness of highly active antiretroviral therapy (HAART) in preventing disease progression can be negatively influenced by the high prevalence of substance use among patients. Here, we quantify the effect of history of injection drug use and alcoholism on virologic and immunologic response to HAART. Clinical and survey data, collected at the start of HAART and at the interview date, were based on the study Longitudinal Investigations into Supportive and Ancillary Health Services (LISA) in British Columbia, Canada. Substance use was a three-level categorical variable, combining information on history of alcohol dependence and of injection drug use, defined as: no history of alcohol and injection drug use; history of alcohol or injection drug use; and history of both alcohol and injection drug use. Virologic response (pVL) was defined by ≥ 2 log10 copy/mL drop in a viral load. Immunologic response was defined as an increase in CD4 cell count percent of ≥ 100%. We used cumulative logit modeling for ordinal responses to address our objective. Of the 537 HIV-infected patients, 112 (21%) were characterized as having a history of both alcohol and injection drug use, 173 (32%) were nonadherent (<95%), 196 (36%) had a CD4⁺/pVL⁺ (Best) response, 180 (34%) a CD4⁺/pVL⁻ or a CD4⁻ /pVL⁺ (Incomplete) response, and 161 (30%) a CD4⁻ /pVL⁻ (Worst) response. For individuals with history of both alcohol and injection drug use, the estimated probability of non-adherence was 0.61, and (0.15, 0.25, 0.60) of Best, Incomplete and Worse responses, respectively. Screening and detection of substance dependence will identify individuals at high-risk for nonadherence and ideally prevent their HIV disease from progressing to advanced stages where HIV disease can become difficult to manage.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".