Stay It with Flora: Maintaining Vaginal Health as a Possible Avenue for Prevention of Human Immunodeficiency Virus Acquisition
Bibliographic record
Abstract
mucosal surface, the vagina may be colonized by a variety of bacteria, fungi, and other potential pathogens. A normal vaginal flora is predominately composed of Lactobacillus species, with low diversity [1]. Lactobacilli provide significant health benefits to the host by decreasing the vaginal pH through lactic acid production, generating H2O2 and bacteriocins, and stimulating the local immune system [2], all of which serve as barriers to pathogens. Thus, an abnormal vaginal flora may have significant implications for the transmission of human immunodeficiency virus (HIV) and other sexually transmitted infections (STIs). It is therefore unfortunate that, in the real world, a normal (i.e., lactobacilli-predominant) vaginal flora is not the norm. The composition of the vaginal flora within a population constitutes a spectrum. In some individuals, H2O2producing Lactobacillus species predominate, whereas in others, the flora may include an increasing proportion of various species, such as Gardnerella vaginalis, Mycoplasma hominis, and gramnegative and gram-positive anaerobes, including Prevotella organisms [1, 3]. This spectrum is often quantified numerically by means of the Nugent scoring system, with flora classified as normal, intermediate, or bacterial vaginosis [4]. On the basis of this system, the vaginal flora is actually abnormal (i.e., it is classified as intermediate or vaginosis) in at least half of women. This abnormal status
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".