Bibliographic record
Abstract
In British Columbia, Canada, intensive efforts have been made to implement and maintain a treatment-as-prevention strategy among the HIV-infected population. Acceleration of antiretroviral therapy coverage has resulted in a substantial increase in the median CD4+ cell count at which treatment is initiated and a dramatic decline in community plasma HIV RNA levels. This has resulted in a reduction in diagnoses of new cases of HIV infection, including among injection drug users. Proportions of individuals with viral suppression have steadily increased and the expansion of antiretroviral therapy coverage has not been associated with increased levels of HIV resistance. Further, adoption of routine HIV testing in acute care settings has been very well accepted and has captured new cases at a rate of 5 per 1000 tests outside of high-risk populations, offering an additional strategy for identifying and linking at least some individuals with undiagnosed HIV infection to care. Deriving optimal individual and social health outcomes in HIV infection requires improvement in every element of the cascade of care. This article summarizes a presentation by Julio S. G. Montaner, MD, at the IAS-USA continuing education program held in San Francisco, California, in March 2013.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".