Obesity, Early Life Gut Microbiota, and Antibiotics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Obesity is a major public health problem that continues to be one of the leading risk factors for premature death. Early life is a critical period of time when the gut microbiota and host metabolism are developing in tandem and significantly contribute to long-term health outcomes. Dysbiosis of the gut microbiota, particularly in early life, can have detrimental effects on host health and increase the susceptibility of developing obesity later in life. Antibiotics are an essential lifesaving treatment; however, their use in early life may not be without risk. Antibiotics are a leading cause of intestinal dysbiosis, and early life administration is associated with obesity risk. The following review explores the relevant literature that simultaneously examines antibiotic-induced dysbiosis and obesity risk. Current evidence suggests that disruptions to the composition and maturation of the gut microbiota caused by antibiotic use in early life are a key mechanism linking the association between antibiotics and obesity. Without compromising clinical practice, increased consideration of the long-term adverse effects of antibiotic treatment on host health, particularly when used in early life is warranted. Novel adjunct interventions should be investigated (e.g., prebiotics) to help mitigate metabolic risk when antibiotic treatment is clinically necessary.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it