Bibliographic record
Abstract
Highly-active antiretroviral (ARV) therapy (HAART) has lead to a sharp decline in AIDS-related morbidity and mortality. Treatment failure is a common, significant problem and as many as 50% of patients have detectable plasma HIV RNA despite being on combination ARV therapy. Clinicians must be knowledgeable about the reasons for treatment failure and the best options available for management. Treatment failure can occur because of non-compliance, drug discontinuation, lack of drug potency, inadequate drug plasma concentration and drug resistance. Strategies used when selecting salvage therapy include the use of resistance testing to choose a regimen, the exploitation of pharmacokinetic interactions by boosting protease inhibitor (PI) trough levels and counselling the patient on compliance. When selecting the agents to use in salvage therapy, the new regimen should ideally include as many new agents to which no or minimal resistance is anticipated and at least one new class of drugs if possible. Data on salvage therapy mostly comes from anecdotal reports and retrospective cohort studies. With a paucity of clinical trial data, clinicians are often forced to prescribe unproven regimens based on what is anticipated about cross-resistance and drug interactions. It is important that new agents and new targets continue to be developed as an increasing number of patients in practice have exhausted all treatment options.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".