Bibliographic record
Abstract
Antibiotics have transformed modern medicine. They are essential for treating infectious diseases and enable vital therapies and procedures. However, despite this success, their continued use in the 21st century is imperiled by two orthogonal challenges. The first is that the microbes targeted by these drugs evolve resistance to them over time. The second is that antibiotic discovery and development are no longer cost-effective using traditional reimbursement models. Consequently, there are a dwindling number of companies and laboratories dedicated to delivering new antibiotics, resulting in an anemic pipeline that threatens our control of infections. The future of antibiotics requires innovation in a field that has relied on highly traditional methods of discovery and development. This will require substantial changes in policy, quantitative understanding of the societal value of these drugs, and investment in alternatives to traditional antibiotics. These include narrow-spectrum drugs, bacteriophage, monoclonal antibodies, and vaccines, coupled with highly effective diagnostics. Addressing the antibiotic crisis to meet our future needs requires considerable investment in both research and development, along with ensuring a viable marketplace that encourages innovation. This review explores the past, present, and future of antimicrobial therapy.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".