Prognostic Staging of Extensively Pretreated Patients with Advanced HIV-1 Disease
Bibliographic record
Abstract
PURPOSE: Determinants of therapeutic success are poorly characterized in patients with extensive HAART experience. Positive prognostic factors (PPFs) in the TORO trials could serve as the basis for a prognostically meaningful staging of treatment-experienced patients initiating a new antiretroviral regimen. METHOD: In TORO, triple-class-experienced patients with viral load (VL) > or = 5,000 copies/mL received an optimized background regimen of 3-5 antiretrovirals (based on treatment history and baseline resistance testing) +/- enfuvirtide (n = 995). Clinically relevant baseline PPFs that were predictive of 48-week virologic outcomes were identified via multiple regression analyses. RESULTS: The likelihood of VL < 400 copies/mL at 48 weeks (ITT analysis) was greater for those patients who had baseline CD4 count > or = 100 cells/mm3 (odds ratio [OR] 2.1; 95% confidence intervals [CIs] 1.5, 3.1); baseline VL < 5 log10 copies/mL (OR 1.8; 95% CIs 1.2, 2.6); received < or = 10 prior antiretrovirals (OR 2.4; 95% CIs 1.6, 3.4); or received > or = 2 active antiretrovirals in their background treatment regimen (OR 2.3; 95% CIs 1.6, 3.3). Overall, 67% of triple-class-experienced patients who met all four prognostic criteria and received enfuvirtide achieved VL < 400 copies/mL at 48 weeks vs. 43% for non-enfuvirtide patients (p < .05). Similar results were obtained when the analysis was done separately in each of the randomization arms of the study. CONCLUSION: Our findings provide guidance for physicians on expected outcomes in treatment-experienced patients and should be of value in their clinical management, as well as in stratifying participants in clinical trials involving treatment-experienced patients.
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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".