Bibliographic record
Abstract
Seminal work by Mellors et al1 clearly demonstrated that CD4 cell counts and plasma HIV RNA levels are key prognostic indicators among untreated individuals infected with HIV. CD4 cell counts and plasma HIV-1 RNA levels are widely accepted as markers of vulnerability to opportunistic diseases and subsequent mortality in this setting.1-3 Coffin4 compared the contribution of these parameters to the risk of disease progression related to HIV/AIDS to a steam engine careening toward an impending wreck, with the plasma HIV RNA level representing the speed of the train and the CD4 cell count representing the length of track remaining. Highly active antiretroviral therapy (HAART) first emerged at the International AIDS Society-sponsored Vancouver AIDS Conference in 1996. Since then, HAART has been shown to suppress the circulating levels of plasma HIV RNA dramatically, thereby bringing the metaphoric steam engine to a halt.5 Suppression of plasma HIV RNA leads to CD4 cell count rebound6 and, in turn, dramatic reductions in HIV-related morbidity and mortality.7-9 The impressive effects of antiretroviral therapy initially led to a “hit early/hit hard”10 approach in which HIV treatment guidelines recommended initiation of antiretroviral therapy when the CD4 count decreased to less than 500 cells/μL or the plasma HIV/RNA level rose to greater than 30,000 copies/mL.11 Because eradication of HIV infection is not considered possible with presently available therapeutic agents,12 those undergoing treatment for HIV disease must take a daily regimen of at least 3 antiretroviral agents.13 Near-perfect levels of adherence to the regimen are required to suppress the plasma HIV RNA level in a sustained fashion.14,15 Furthermore, incomplete adherence has been associated with rapid emergence of antiretroviral resistance.16,17 Although simpler and more tolerable HAART regimens are being developed, the long-term side effects of HAART may be substantial.18-21 These challenges have led to a recent shift away from the hit early/hit hard approach to a strategy in which patients are encouraged to delay the initiation of HAART until the CD4 count falls to or less than 350 cells/μL but before the CD4 count reaches 200 cells/μL.22 These thresholds have been derived from prospectively monitored cohorts of HIV-infected individuals initiating HAART that clearly demonstrate a survival advantage when HAART is initiated with CD4 cell counts greater than 200 cells/μL, whereas no such advantage is seen if HAART is initiated with CD4 cell counts greater than 350 cells/μL. Most of these studies have focused on the impact of HAART, initiated at various CD4 cell counts and plasma HIV RNA levels, on survival, whereas issues other than survival have received little attention.22 In this issue, Gras and colleagues23 use data from the AIDS Therapy Evaluation Project, Netherlands (ATHENA) to demonstrate elegantly that many patients on long-term HAART regain CD4 counts greater than 800 cells/μL. More importantly, they demonstrate a strong association between pretreatment CD4 cell count and enhanced CD4 cell count recovery among patients initiating HAART with higher CD4 cell counts. Among patients with a baseline CD4 count of 200 to 350 cells/mm3, 46% reach 800 cells/mm3, whereas this jumped to 73% among those with baseline CD4 cell counts of 350 to 500 cells/mm3. The clinical implications of these findings have yet to be determined, because most HIV-related complications are not observed until the CD4 cell count declines to less than 200 cells/mm3.24 Reports from the Data Collection on Adverse Events of Anti-HIV Drugs (DAD) study have indicated that elevated risk of mortality from liver disease and certain cancers is seen at lower CD4 cell counts.25 Of interest, this study noted differences in these specific endpoints even between those in the 200 to 350 cells/mm3 and >350 cells/mm3 categories. Similarly, the recently reported results from the Strategies for Management of Antiretroviral Therapy (SMART) trial have indicated increased risk of disease progression in the CD4 cell count-guided episodic antiretroviral therapy arm in comparison to the continuous antiretroviral therapy arm despite the fact that almost all participants in the trial had CD4 counts greater than 200 cells/mm3 during follow-up.26 Interestingly, although HAART is associated with the range of side effects described previously, the SMART trial indicated that patients in the CD4 cell count-guided interrupted antiretroviral therapy arm had increased risk of certain health outcomes (combined endpoint: myocardial infarction, stroke, liver cirrhosis, and renal failure) compared with those on continuous HAART treatment. As HAART evolves over time, newer regimens tend to be simpler and safer. This progressively opens the door for a broader re-evaluation of the ideal time to start therapy, incorporating outcomes other than survival, such as the level of immune reconstitution demonstrated by Gras et al.23 In addition, there is growing evidence that HAART-treated patients are less likely to transmit HIV infection to others.27 Recently, mathematic modeling has suggested that expansion of HAART programs could play a substantial role in decreasing HIV incidence.28 So far, the added preventive value of HAART has not been incorporated into the equation when evaluating the ideal time to start therapy. Given the potential effect that HAART can have in various domains other than survival, however, we must broaden our focus to incorporate a variety of appropriately weighted patient outcomes, and their public health consequences. ACKNOWLEDGMENTS The authors thank Deborah Graham, Kelly Hsu, and Peter Vann for their research and administrative assistance.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.105 | 0.052 |
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".