Bibliographic record
Abstract
For the survival of humankind, nothing can be as important as the health of a mother and a child. As the world's population grows to more than 6 billion, it might seem ridiculous to suggest that any real threat exists to the human species. Diseases have long ravaged populations, as have wars, poverty, and malnutrition. Life today is no different with new and emerging diseases such as SARS and Mad Cow Disease leaving a trail of concern around the planet. All that being said, the AIDS crisis is threatening humans like no other. In countries such as Botswana, close to half the population of pregnant women is infected. Of great concern, the disease is now prevalent among women and teenage girls, threatening not only their lives but those of their offspring. Efforts to control this spread are quite abysmal, albeit well intentioned. Likewise, the death of a child every 15 seconds from diarrheal disease is not being addressed with the same vigor as SARS, even though the risk of dying from the latter for most people is similar to being struck by lightning. In the end, it is the economy and politics that dictate health spending. Image and perception are everything. While deaths mount among women and children from AIDS and other infections, the potential to intervene with a low-risk natural concept of probiotics seemed all too distant. As evidence mounts of the attributes of certain probiotic strains to treat diarrhea and reduce the risk of urogenital and other diseases, the developing world has failed to embrace it, support its evaluation and implementation, and take it to where it is needed the most. In this review, the case for and against probiotics for urogenital and intestinal infections is presented based on current literature. The story is far from complete, but the potential for improving the health of the mother and child is significant. United Nations and World Health Organization guidelines have now been developed to vanquish the unproved marketing hype products that have given probiotics a bad name. It is now up to science to deliver the remedies, and to society to make sure that only proven products reach the marketplace and the people in most need.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.045 | 0.016 |
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".