Q&A: Antibiotic resistance: where does it come from and what can we do about it?
Bibliographic record
Abstract
Yes. Historically, the discovery of the sulfa drugs in the 1930s and the subsequent development of penicillin during World War II ushered in a new era in the treatment of infectious diseases. Infections that were common causes of death and disease in the pre-antibiotic era -rheumatic fever, syphilis, cellulitis and bacterial pneumonia -became treatable, and over the next 20 years most of the classes of antibiotics that find clinical use today were discovered and changed medicine in a profound way. The availability of antibiotics enabled revolu tionary medical interventions such as cancer chemo therapy, organ transplants and essentially all major invasive surgeries from joint replacements to coronary bypass. Antibiotics, though, are unique among drugs in that their use precipitates their obsolescence. Paradoxically, these cures select for organisms that can evade them, fueling an arms race between microbes, clinicians and drug discoverers.
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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.065 | 0.046 |
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".