Bibliographic record
Abstract
In his News Focus story (“Treatment as prevention,” 5 March, p. [1196][1]), J. Cohen reviews ideas presented at the 17th Conference on Retroviruses and Opportunistic Infections about the use of HIV treatment as prevention. Enthusiasm for the treatment-as-prevention approach has grown in recent years as (i) the drugs have become safer, better tolerated, and more widely available; (ii) widespread testing has become cheaper and more efficient; (iii) earlier therapy has become desirable; and (iv) mathematical modeling by some ([ 1 ][2]) (but by no means all) has suggested that a test-and-treat strategy could control the spread of HIV. Cohen cites an observational analysis, by Donnell et al. , that reported considerable reduction of HIV transmission in HIV discordant couples when ART was provided to the HIV-infected index partner ([ 2 ][3]). This finding—similar to work from Sullivan et al. presented at Conference on Retroviruses and Opportunistic Infections in 2009 ([ 3 ][4])—helps to support the key assumption that ART reduces infectiousness. However, these studies report only short-term observations; they do not address the durability of this effect or the risk of transmitted drug-resistant HIV strains, two critical considerations for the test-and-treat strategy. 1. [↵][5] 1. R. M. Granich, 2. C. F. Gilks, 3. C. Dye, 4. K. M. De Cock, 5. B. G. Williams , Lancet 373, 48 (2009). [OpenUrl][6][CrossRef][7][PubMed][8][Web of Science][9] 2. [↵][10] 1. D. Donnell 2. et al ., abstract 136, presented at the 17th Conference on Retroviruses and Opportunistic Infections, San Francisco, CA, 16 to 19 February 2010. 3. [↵][11] 1. P. Sullivan 2. et al ., abstract 52bLB, presented at the 16th Conference on Retroviruses and Opportunistic Infections, Montreal, Canada, 8 to 11 February 2009. [1]: /lookup/doi/10.1126/science.327.5970.1196-b [2]: #ref-1 [3]: #ref-2 [4]: #ref-3 [5]: #xref-ref-1-1 View reference 1 in text [6]: {openurl}?query=rft.jtitle%253DLancet%26rft.stitle%253DLancet%26rft.aulast%253DGranich%26rft.auinit1%253DR.%2BM.%26rft.volume%253D373%26rft.issue%253D9657%26rft.spage%253D48%26rft.epage%253D57%26rft.atitle%253DUniversal%2Bvoluntary%2BHIV%2Btesting%2Bwith%2Bimmediate%2Bantiretroviral%2Btherapy%2Bas%2Ba%2Bstrategy%2Bfor%2Belimination%2Bof%2BHIV%2Btransmission%253A%2Ba%2Bmathematical%2Bmodel.%26rft_id%253Dinfo%253Adoi%252F10.1016%252FS0140-6736%252808%252961697-9%26rft_id%253Dinfo%253Apmid%252F19038438%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [7]: /lookup/external-ref?access_num=10.1016/S0140-6736(08)61697-9&link_type=DOI [8]: /lookup/external-ref?access_num=19038438&link_type=MED&atom=%2Fsci%2F328%2F5981%2F976.atom [9]: /lookup/external-ref?access_num=000262183800031&link_type=ISI [10]: #xref-ref-2-1 View reference 2 in text [11]: #xref-ref-3-1 View reference 3 in text
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 | 0.064 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.034 | 0.056 |
| Insufficient payload (model declined to judge) | 0.019 | 0.012 |
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".