Lessons learned from the prenatal microbiome controversy
Bibliographic record
Abstract
For more than a century, the prenatal environment was considered sterile. Over the last few years, findings obtained with next-generation sequencing approaches from samples of the placenta, the amniotic fluid, meconium, and even fetal tissues have challenged the dogma of a sterile womb, and additional reports have emerged that used culture, microscopy, and quantitative PCR to support the presence of a low-biomass microbial community at prenatal sites. Given the substantial implications of prenatal exposure to microbes for the development and health of the host, the findings have gathered substantial interest from academics, high impact journals, the public press, and funding agencies. However, an increasing number of studies have challenged the prenatal microbiome identifying contamination as a major issue, and scientists that remained skeptical have pointed to inconsistencies with in utero colonization, the impact of c-sections on early microbiome assembly, and the ability to generate germ-free mammals. A lively academic controversy has emerged on the existence of the wider importance of prenatal microbial communities. Microbiome has asked experts to discuss these issues and provide their thoughts on the implications. To allow for a broader perspective of this discussion, we have specifically selected scientists, who have a long-standing expertise in microbiome sciences but who have not directly been involved in the debate so far.
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 | 0.028 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.013 | 0.028 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".