Outgrowing the Immaturity Myth: The Cost of Defending From Neonatal Infectious Disease
Bibliographic record
Abstract
and (ii) the ability of neonates to survive a significantly higher blood pathogen burden than of adults. To reconcile the apparent contradiction of clinical susceptibility to disease and the host immune response findings when contrasting newborn to adult, it will be essential to capture the entirety of available host-defense strategies at the newborn's disposal. Adults focus heavily on the disease resistance approach: pathogen reduction and elimination. Newborn hyperactive innate immunity, sensitivity to immunopathology, and the energetic requirements of growth and development (immune and energy costs), however, preclude them from having an adult-like resistance response. Instead, newborns also may avail themselves of disease tolerance (minimizing immunopathology without reducing pathogen load), as a disease tolerance approach provides a counterbalance to the dangers of a heightened innate immunity and has lower-associated immune costs. Further, disease tolerance allows for the establishment of a commensal bacterial community without mounting an unnecessarily dangerous immune resistance response. Since disease tolerance has its own associated costs (immune suppression leading to unchecked pathogen proliferation), it is the maintenance of homeostasis between disease tolerance and disease resistance that is critical to safe and effective defense against infections in early life. This paradigm is consistent with nearly all of the existing evidence.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".