<i>Editorial Commentary</i>: Unmasking the Bare Bones of HIV Preexposure Prophylaxis
Bibliographic record
Abstract
(See the Major Article by Mulligan et al on pages 572–80.) 2015 marks an important milestone in the struggle against human immunodeficiency virus (HIV)/AIDS. Almost 20 years ago, effective combination antiretroviral therapy (cART) became available and significantly altered the outcome of HIV infection, transforming an essentially uniformly fatal infection into a manageable, chronic illness that continues to challenge patients, care providers, and the scientific community [1]. People still become infected with HIV [2, 3] as effective prevention remains elusive. In the absence of an HIV vaccine, other means of HIV prevention have been investigated. The practical application of the “treatment as prevention” hypothesis significantly decreases new infections in at-risk individuals [4]. Antiretrovirals are highly effective for preexposure prophylaxis (PrEP) of HIV infection [5]. It is anticipated that PrEP, as part of a multipronged program of HIV prevention options, will impact the incidence of new infections. PrEP's safety profile is a particularly important consideration as a primary prevention therapy. It is thus noteworthy that the available regimen for PrEP, the daily use of a fixed-dose combination of oral emtricitabine/tenofovir disoproxil fumarate (FTC/TDF), contains tenofovir (TFV). TFV causes renal toxicity, via a proximal tubulopathy, and bone demineralization, by increasing bone turnover and altering bone metabolism, in a minority of patients [6]. In general, low bone mineral density (BMD) occurs more frequently in treated HIV adults than in seronegative individuals [7]. Low BMD is associated with an increased risk of fragility fractures [8, 9]. The etiology of low BMD is multifactorial, including both HIV- and cART-related factors [10], plus common causes of low BMD, as occurs in seronegative persons [11], including low body weight [12]. Currently available cART regimens are associated with a BMD decline of 4%–6%, generally occurring in the 4–6 months after starting treatment, and which is nonprogressive [10]. TFV-HAART is associated with greater BMD loss compared with non-TFV regimens [7]. Significantly, TFV-HAART has been associated with an increased fracture risk compared with other regimens in some studies [13].
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.023 | 0.023 |
| Insufficient payload (model declined to judge) | 0.017 | 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".