Bibliographic record
Abstract
Sir—We thank Lawn et al. [1] for their comments regarding our meta-analysis of antiretroviral therapy programs in resource-poor settings [2]. Although we agree that use of viral load suppression provides a limited outcome assessment of any antiretroviral treatment program, our analysis was limited at the time by a paucity of data on other outcomes from low-income countries. Our intention was to perform meta-analyses on as many outcome measures as had data available and to broadly compare aspects of treatment programs. Table 1 in our article [2] displays the outcome data that were available for the 10 studies, and includes data on probability of survival or probability of an AIDS-free event. This measure was available for only 5 of the 10 studies, however, and the data were reported at different timepoints in different studies—facts which essentially prohibited a meaningful meta-analysis of this outcome measure. Although viral load suppression is just one component of program success, it demonstrates drug efficacy and also (importantly, in our view) demonstrates that doubts regarding capacity to deliver antiretrovirals or the ability of persons to adhere to medications in poor countries are not justified—these are not the major obstacles to effective HIV care. In the past, such misconceptions caused significant delays in the international community's response to the HIV/AIDS epidemic in low- and middle-income countries.
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 | 0.010 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.104 | 0.066 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".