CAHR 2007 ‐ Oral Sessions
Bibliographic record
Abstract
Objectives: To assess the impact of scaling up HAART on the HIV epidemic by simultaneously tracking the transmission dynamics of different sources of infection.Methods: We built a semi-deterministic dynamic model.The HIV natural history was defined by different infectivity strata: susceptible; primary infection; symptomatic phase defined by four viral load strata (<3, 3-4, 4-5, ≥5 log 10 copies/mL); and late stage.The model's deterministic component was responsible for predicting the number of new infections by increasing HAART coverage from 50% to 75%, 90% and 100%, and by changing the CD4 threshold for therapy initiation from ≤200 cells/mm 3 to ≤350 cells/mm 3 .The random component of this model was modeled using generalized additive models to take into account the effect of HAART, adherence, resistance and other key clinical and demographic factors on the distribution of individuals amongst infectivity strata over time.The probability of emergence of resistance during the course of therapy was modeled using logistic regression.Results: Our model predicts that within 25 years, given the current guidelines and adherence level (78.5%), the cumulative number of new infections averted by increasing the number of people on treatment from 50% to 75%, 90% and 100% is, respectively, 3108, 4776, and 5701.The greatest impact was on the new infections driven by injection drug use.These numbers are further stratified by different adherence levels.A beneficial effect was also seen in the worst case scenarios -high probability of developing resistance, and high virulence because of low adherence levels.Conclusions: Higher HAART usage was inevitably succeeded by a small increase in the prevalence of individuals carrying drug resistant virus driven by imperfect adherence and consequently high viral load.However, the overall benefit of expanding the access to HAART, as a powerful prevention strategy, was overwhelmingly substantial in reducing the growth of the epidemic.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.767 | 0.548 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".