Bibliographic record
Abstract
May 2002 When to Start Antiretroviral Therapy It is not clear when antiretroviral therapy (ART) should be started in asymptomatic patients. The decision to start ART is usually guided by a low CD4 count or a high viral load. Opportunistic infections do not usually occur until the CD4 count has fallen below 200 cells per ul or the viral load has reached 10,000 copies per ml. The medications are costly and produce undesirable side effects. However, early intervention might better preserve the patient’s immune status and reduce infectivity, among other benefits. Two recent studies shed some light on this issue. European Observational Study A multi-center observational European study (Phillips AN et al, JAMA 2001;286:2560–7) enrolled over 3,000 patients who had not received any ART and evaluated the response to treatment, stratified by initial CD4 count (< 200, 200–349, > 350 cells per ul), with at least three drugs, beginning in 1996. The median follow-up period was over 2 years. Patients were as likely to reach viral loads of less than 500 copies per ml after 32 weeks of treatment at all three CD4 ranges and irrespective of the baseline viral load unless that value was >100,000 copies per ml. The authors noted a tendency for higher rates of new AIDS diseases or death during follow-up for the lowest CD4 range (< 200) but not between the two higher ranges. As the authors point out, there may be other advantages, not measured in this study, of starting earlier: e.g., the likelihood of complete immune recovery might be greater if treatment is started earlier. Canadian Study This study, like the European one, involved ART-naïve patients who were started on triple-drug ART from 1996–1999. The endpoint was death, and the 1,219 patients were stratified by CD4 cell count and viral load. Multivariate analysis showed that, of various potential predictors of survival, only CD4 cell count was statistically significant. The adjusted risk ratios for death were 6.67 for those with baseline CD4 counts of > 50 cells/ul and 3.41 for those with counts of 50–199. Viral load was not an independent predictor of survival. The results were similar when progression to AIDS, rather than death, was used as the endpoint. Three previous cohort studies also have shown that disease progression to AIDS or death tended to be clustered among patients initiating ART with CD4 cell counts < 200 and that baseline viral loads were not predictive of mortality during treatment. The authors emphasize that their study does not allow one to infer an optimal time to begin treatment. In particular, it is possible that longer-term follow-up might show differences in outcome among patients with baseline CD4 counts over 200. The authors suggest that, in the decision to initiate ART, the focus should be on the CD4 cell count and that treatment generally should be started before the count reaches 200 cell/ul. An editorial (Pomerantz RJ, ibid, p 2597–9) agrees that, based on the available data, it may be reasonable to focus on the CD4 count in determining the time to initiate ART and to try to start before the count is below 200 cells per ul. It also points out that for patients seen within 6 months after seroconversion, immediate initiation of potent ART may preserve HIV-specific T-helper cell function. Randomized Trial to Begin The trials noted above were observational and relatively short-term. A long-term, randomized study, called Strategies for Management of Antiretroviral Therapies (SMART) will shortly begin (IDSA News, February, 2002, pg 11). It will involve 6,000 patients in 21 United States locations and several Australian sites who will be followed for up to 9 years. The primary goal will be to compare the outcomes of patients treated early in HIV infection as opposed to later, using the CD4 count as a critical marker. The trial will involve community-based researchers and will be funded by the National Institute of Allergy and Infectious Diseases. Contact information is available at www.clinicaltrials.gov. The search term SMART may be used.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.019 |
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; both teacher heads 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".