Antimicrobial resistance and the growing threat of drug-resistant tuberculosis
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is a global health emergency, and experts are concerned that the end of the age of antimicrobials is imminent [1–6]. Since the introduction of antimicrobials nearly a century ago, microbes have evolved a variety of methods to resist these drugs. Today, the world is dealing with ‘superbugs’ that are virtually untreatable, including drug-resistant gonorrhea, carbapenem-resistant enterobacteriaceae, Methicillin-resistant Staphylococcus aureus, and extended-spectrum-beta-lactamase producing strains [5]. The antibiotic pipeline is running dry, and AMR is threatening to undo major gains made in the control of infectious diseases. Models suggest that 300 million people are expected to die prematurely because of AMR over the next 35 years and the world’s GDP will be 2–3.5% lower than it otherwise would be in 2050 [6]. This translates into a loss of 60–100 trillion USD worth of economic output by 2050.[...]
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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.022 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".