Bibliographic record
Abstract
Our perception of microbes has considerably changed since the recognition of their pathogenic potential in the 19th century. The discovery of antibiotics and their subsequent widespread adoption have substantially altered the landscape of medicine, providing us with treatment options for many infectious diseases and enabling the deployment of previously risky interventions (eg, surgical procedures and chemotherapy), while also leading to the rise of AMR. The latter is commonly viewed as the predominant downside of antibiotic use. However, with the increasing recognition that all metazoan organisms rely on a community of microbes (the microbiota) for normal development and for most physiologic processes, the negative impacts of antibiotic use now extend well beyond AMR. Using the iceberg as a metaphor, we argue that the effects of antibiotics on AMR represent the tip of the iceberg, with much greater repercussions stemming from their role in the rise of so-called noncommunicable diseases (including obesity, diabetes, allergic and autoimmune diseases, neurodevelopmental disorders, and certain cancers). We highlight some of the emerging science around the intersection of the microbiome, antibiotic use, and health (including biological costs and future therapeutic avenues), and we advocate a more nuanced approach in evaluating the impacts of proposed antibiotic use, especially in the setting of preexposure and postexposure prophylaxis.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".