Guidelines for the management of HIV infection in pregnant women and the prevention of mother‐to‐child transmission
Bibliographic record
Abstract
AIMS OF THE GUIDELINES: These guidelines, drawn up by a multidisciplinary group of clinicians and lay workers active in the management of pregnant women infected with HIV, aim to give up-to-date information on interventions to reduce the risk of mother to child transmission of the virus. The evidence on the use of interventions to prevent mother to child transmission of HIV has been graded according to the strength of the data as per the definitions of the US Agency for Health Care Policy and Research [1]. Weighted evidence on the use of combination antiretroviral therapy (ART) for the treatment of HIV infection per se is presented in the BHIVA guidelines for adults [2,3]. The highest level evidence (i.e. randomised controlled trials (RCTs) or large, well conducted meta-analyses) is only available for formula feeding, prelabour caesarean section and zidovudine monotherapy. The need to treat mothers for HIV infection has led to the widespread use of ART in pregnancy which in turn results in new questions such as how to deliver when the mother, on therapy, has no detectable plasma viraemia with the most sensitive assays. In addressing many common and/or difficult clinical scenarios in the absence of 'best evidence' the guidelines rely heavily on 'expert opinion'. Recommendations for management are given in the section on clinical scenarios, and summarized in Table 3. An expanded version of these guidelines with an appendix on safety and toxicity data is available on the BHIVA website http://www.bhiva.org. The authors are available to discuss individual cases.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.026 | 0.017 |
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".