(See the article by Henrich et al, on pages 93–98.) There now are 12 dozen US Food and Drug Administration–, Health Canada–,
Bibliographic record
Abstract
roviral agents from 6 distinct classes that are used in combination for the treatment of human immunodeficiency virus (HIV) infection. These drugs have been intro-duced at the rate of ∼1 per year, and that rate seems likely to continue for at least the next 5 years. Although guidelines based largely on results of randomized clinical trials exist to aid in the selection of certain combinations under certain cir-cumstances [1], the staggering number of potential 3- or 4-drug combinations se-lected from 2 or 3 of the 6 different classes effectively precludes knowing which com-bination is the most potent (ie, active). Indeed, it is not known yet which measure of “potency ” (antiviral activity) correlates best with the most frequently used out-come in clinical trials: the percentage of participants with HIV RNA level!50 cop-ies/mL at some time point (typically 6
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.174 | 0.109 |
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".